Instrument and system for rapid microorganism identification and antimicrobial agent susceptibility testing

ABSTRACT

A system for automated microorganism identification and antibiotic susceptibility testing comprising a reagent cartridge, a reagent stage, a cassette, a cassette, stage, a pipettor assembly, an optical detection system, and a controller is disclosed. The system is designed to dynamically adjust motor idle torque to control heat load and employs a fast focus process for determining the true focus position of an individual microorganism. The system also may quantify the relative abundance of viable microorganisms in a sample using dynamic dilution, and facilitate growth of microorganisms in customized media for rapid, accurate antimicrobial susceptibility testing.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.15/085,953, filed Mar. 30, 2016, which claims the benefit of U.S.Provisional Application No. 62/268,340, filed Dec. 16, 2015; U.S.Provisional Application No. 62/260,085, filed Nov. 25, 2015; U.S.Provisional Application No. 62/194,142, filed Jul. 17, 2015; U.S.Provisional Application No. 62/152,773, filed Apr. 24, 2015; and U.S.Provisional Application No. 62/140,300, filed Mar. 30, 2015, all ofwhich are incorporated herein by reference in their entirety.

FIELD

The present disclosure relates to an instrument and system forperforming microorganism identification and antimicrobial susceptibilitytesting.

SUMMARY

The disclosed system is an automated microscopy system designed toprovide rapid and accurate microorganism identification and antibioticsusceptibility testing results. This automated microscopy systemcomprises a reagent cartridge comprising a plurality of wells; a reagentstage, wherein the reagent stage comprises an annular shape defining aninterior opening, wherein the regent stage is configured to rotate in afirst plane, and wherein the reagent stage is configured to receive thereagent cartridge; a cassette comprising a plurality of microfluidicchannels, each of the plurality of microfluidic channels comprising aninlet port configured to receive a pipette tip; a cassette stage locatedwithin the interior opening of the reagent stage, wherein the cassettestage is configured to rotate and move laterally in the first plane, andwherein the cassette stage is configured to receive the cassette; apipettor assembly configured to move a plurality of reagents between theplurality of wells of the reagent cartridge and the inlet ports of eachof the plurality of microfluidic channels; an optical detection systemconfigured to obtain dark field and fluorescence photomicrographs of amicroorganism contained in the plurality of microfluidic channels; and acontroller configured to direct operation of the system and processmicroorganism information derived from photomicrographs obtained by theoptical detection system.

The system may further comprise an optical detection system comprising arapid focus algorithm that calculates the virtual true focus position ofan individual microorganism at a given location within about 500 ms,which via repeated imaging over a period of time, permits identificationof responses of that microorganism to environmental conditions. Thedisclosed system can be used for imaging microorganisms tagged withfluorescent labels, such as fluorescently labeled probes that recognizeand bind to complementary bacterial sequences. In some embodiments, thesystem accomplishes this in a process that comprises utilizing customimage analysis software to assign unique spatial XY coordinates toindividual microbes bound to labeled probes and to identifycharacteristics of them. Morphological and other data obtained fromcaptured images are inputted to a probability expectation model ofdistribution to identify one or more microorganisms in patient samples.The system may further comprise a Proportional-Integral-Derivative(“PID”) controller algorithm that dynamically adjusts motor idle torqueto control heat load. Moreover, following identification ofmicroorganisms, the system may subject identified microorganisms toantimicrobial susceptibility analysis. For example, the microorganismscan be grown in Mueller-Hinton nutrient-depleted media to differentiateantimicrobial-resistant cells from filamentous,antimicrobial-susceptible cells, often within about 12 hours of growth.Likewise, fastidious microorganisms may be grown in 1% phytone tryptoseMueller Hinton Agar for determination of antimicrobial susceptibilityand/or minimum inhibitory concentration of antimicrobial agents. Thesystem may further comprise a system comprising a dynamic dilutionalgorithm that determines a target dilution factor for a sample toachieve dilution of the sample to a target concentration (such as numberof cells per field of view or number of clones or colonies per field ofview).

Features of the system are described in more detail in U.S. ProvisionalPatent Applications 62/152,773, 62/194,142, and 62/260,085, each ofwhich is incorporated by reference herein in their entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter of the present disclosure is particularly pointed outand distinctly claimed in the concluding portion of the specification. Amore complete understanding of the present disclosure, however, may bestbe obtained by referring to the detailed description and claims whenconsidered in connection with the drawing figures, wherein like numeralsdenote like elements.

FIG. 1 is a perspective view of the instrument.

FIG. 2 is a perspective view of the instrument with a door in an openposition.

FIG. 3 is a perspective view of the instrument from a rear side andshowing the upper enclosure removed from a lower portion of theinstrument.

FIG. 4 is a perspective view of the lower portion of the instrument witha lower cover removed.

FIG. 5 and FIG. 6 are perspective views of a reagent stage.

FIG. 7 is a section view of the reagent stage in elevation.

FIG. 8A is a perspective view of the reagent stage and the cassettestage.

FIG. 8B is a section view of the cassette stage in elevation showing thecassette in relation to the heater.

FIG. 9 and FIG. 10 are sectioned perspective views of the reagent stageand the cassette stage.

FIG. 11 is a section view in elevation of the cassette stage.

FIG. 12 is a perspective view of a portion of the instrument showing thepipettor.

FIG. 13 is a perspective view of the pipettor.

FIG. 14A is a schematic diagram of the illumination system.

FIG. 14B is a perspective view of the instrument showing an illuminator.

FIG. 15 is a perspective view of the illuminator and drives.

FIG. 16 is a perspective view of the illuminator.

FIG. 17 is a side elevation view of the illuminator.

FIG. 18A is a section view of the illuminator in elevation taken alongthe line 18A-18A in FIG. 17.

FIGS. 18B, 18C and 18D are perspective views of another section of theilluminator in elevation, which is taken along the respective lines inFIG. 17 and shown at different angles.

FIG. 19 is a perspective view of the optic system.

FIG. 20 is another perspective view of the optic system showing a lowerside.

FIG. 21 is an exploded perspective view of the optic system.

FIG. 22 is a perspective view of the optic system with the housingremoved.

FIG. 23 is a top plan view of the optic system of FIG. 22.

FIG. 24 is a bottom plan view of the optic system.

FIG. 25 is an end view of the optic system.

FIG. 26 is a perspective view of a reagent cartridge.

FIG. 27 is a perspective view of a reagent cartridge kit that includes areagent cartridge.

FIG. 28 is an end elevation view of the reagent cartridge kit.

FIG. 29 is an opposite end elevation view of the reagent cartridge kit.

FIG. 30 is a side elevation view of the reagent cartridge kit.

FIG. 31 is an exploded view of the reagent cartridge kit showing variousreagent cartridge components, including reagent wells, GEF wells andother components.

FIG. 32 is a top plan view of a lower housing component of the reagentcartridge.

FIG. 33 is a perspective view of the reagent cartridge with an upperhousing component removed.

FIG. 34 is a perspective view of the reagent cartridge showing a seal inplace over the cassette and pipette tip rack.

FIG. 35 is a perspective view of a cassette top.

FIG. 36 is a perspective view of the cassette top showing a bottomsurface.

FIGS. 37A-37C are a collection of section views and detail views offeatures of the cassette.

FIGS. 38-40 are top plan, side elevation and perspective views,respectively, of a glass support.

FIG. 41 is a top plan view of the cassette.

FIG. 42 is a side elevation view of the cassette.

FIG. 43 is bottom plan view of the cassette.

FIG. 44 is a perspective view of the cassette showing the top surface.

FIG. 45 is another perspective view of the cassette showing the bottomsurface.

FIG. 46 is an exploded perspective view showing the cassette top, theglass support and a laminate layer.

FIG. 47A is a plan view of the cassette showing a detail of the alignedfiducial holes.

FIG. 47B is a magnified view of the fiducial holes detail in FIG. 47A.

FIG. 48A is a plan view of the cassette showing a detail of the alignedfiducial marks on the glass support and the laminate layer.

FIG. 48B is a magnified view of the detail in FIG. 48A.

FIG. 49 is a section view in elevation of the glass support.

FIGS. 50 and 51 are perspective views of the cassette where the glasssupport has been removed to show the laminate layer.

FIGS. 52A and 53A are partial perspective views illustrating a cassettebefore and after ejector pads have been removed.

FIGS. 52B and 53B are partial perspective views illustrating a cassettebefore and after pockets have been added.

FIGS. 52C and 53C are partial perspective views illustrating a cassettebefore and after chamfers have been added.

FIG. 54 is a perspective view of a laminate layer having a firstthickness.

FIG. 55 is a perspective view of a laminate layer having a secondthickness.

FIGS. 56-58 are plan views of portions of the cassette magnified to showdetails of the flow channels.

FIGS. 59 and 60 are schematic cross-sectional views in elevation of thecassette channel according to two implementations.

FIG. 61 is a top plan view of a cassette showing channels in thecassette top and in the laminate.

FIG. 62 is a top plan view of a cassette showing channels only in thelaminate.

FIG. 63 is a bottom plan view of the cassette of FIG. 61.

FIG. 64 is a bottom plan view of the cassette of FIG. 62.

FIG. 65 is a plan view of the cassette following final assembly.

FIG. 66 is a series of images showing representative FISH ID image dataacquired with the instrument.

FIG. 67 is a series of images for susceptible and resistantmicroorganisms at four different times.

FIG. 68 is a series of graphs showing microorganism growth curves.

FIGS. 69-75 are diagrams of system architectures and process flows for asystem and method of operation in accordance with various embodiments.

FIG. 76 is a schematic diagram of an alternative optical system.

FIG. 77 is a graph of resolution plotted against objective position.

FIG. 78 is a drawing showing a point source image on one side of theimage plane for a sample feature of interest (e.g., a microbe).

FIG. 79 is a drawing similar to FIG. 78, but showing a point sourceimage on an opposite side of the image plane for the sample feature.

FIGS. 80A and 80B are graphs of Spot Area versus focus position andintensity weighting versus focus position.

FIG. 80C is a flowchart of a method for a discovery phase for generatingdata used for autofocusing.

FIG. 81A is a graph showing feedback control of the enclosuretemperature.

FIG. 81B is a flowchart according to one embodiment for controllingenclosure temperature using stepper motors.

FIG. 82A is a flowchart of the focusing algorithm according to oneembodiment.

FIG. 82B is a flowchart of the focusing algorithm according to anotherembodiment.

FIG. 82C is a flowchart of the focusing algorithm according to stillanother embodiment.

FIG. 83 is a series of calibration curves for the output range ofKlebsiella pneumoniae, showing a simple linear dilution curve, a curvecalculated by dynamic dilution, and a curve calculated by dynamicdilution including a 20% growth factor.

FIGS. 84A-84D are a series of normal probability plots comparing theeffect of sample dilution of simple linear dilution curves (● and ◯) andthree-point dilution curves utilizing a growth factor (Δ and ▴) forAcinetobacter baumannii (FIG. 84A), Pseudomonas aeruginosa (FIG. 84B),Klebsiella pneumoniae (FIG. 84C), and Serratia marcescens (FIG. 84D).

FIGS. 85A-85F are a series of graphs of results showing growth ofPseudomonas strains in nutrient-depleted media in the presence ofpiperacillin/tazobactam antibiotic. FIGS. 85A-85C plot log of bacterialdark phase intensity versus time. FIGS. 85A and 85B show growth inmedium having approximately 87% reduction in Mueller Hinton nutrients,compared to growth in standard Mueller-Hinton media (FIG. 85C). FIGS.85D-85F show quantitative representation of the data shown in FIGS.85A-85C, respectively, represented as rate of bacterial cell division.

FIG. 86 is a diagram illustrating a fully-automated multiplex automateddigital microscopy system workflow, including automated samplepreparation, ID (identification), AST (antimicrobial susceptibilitytesting) and reporting.

FIGS. 87A-87C illustrate separation of sample impurities, such as lysedblood cells and debris, from bacterial/yeast cells using automated gelelectrofiltration. FIG. 87A: Sample is loaded onto gel with poressmaller than bacterial/yeast cells. FIG. 87B: When a voltage is applied,debris migrates into gel leaving bacteria/yeast behind. FIG. 87C:Voltage is briefly reversed to allow negatively-charged bacteria/yeastto move to the center of the well for ease of retrieval.

FIG. 88A-88B illustrates capture of bacterial/yeast cells on the lowersurface of flowcells (side view) by electrokinetic concentration. FIG.88A: Sample inoculum is introduced into flowcell. FIG. 88B: When anelectric field is applied, negatively-charged bacterial/yeast cellsmigrate to the lower surface where they are captured on thepositively-charged poly-L-lysine capture coating on the lower surface ofthe flowcell. Indium tin oxide (ITO) electrode layers are between thepoly-L-lysine coating and the glass bottom surface.

FIG. 89 illustrates representative images of an Escherichia coli samplein two different test channels with probes targeting E. coli and Proteusspp. Images were taken in dark-field, 647 nm (universal bacterialprobe), and 532 nm (target probes). The universal bacterial probe bindsto all bacterial cells to differentiate bacteria from debris.Co-localization of universal and target probe signals identifies targetbacteria. Images magnified to view individual cells. Scale bar in lowerright image is 10 μm.

FIG. 90 illustrates time-lapse images of methicillin-susceptible S.aureus (MSSA) and methicillin-resistant S. aureus (MRSA) isolatesgrowing in cefoxitin at 0, 1.5, 3, and 4.5 hours. By 4.5 hours,susceptible clones have arrested or lysed while resistant clonescontinue to grow. Images are magnified to show individual bacterialclones. Scale bar in lower right image is 20 μm.

FIG. 91 illustrates computerized clone tracking of individual progenitorbacterial cells (top panels) as they grow into clones of daughter cells,and following multiple division cycles (bottom panels).

FIG. 92 illustrates microscopic images of Staphylococcus aureus (SA),Acinetobacter baumannii (AB) and Pseudomonas aeruginosa (PA). Cellmorphology differences are clearly visible between species and can bedetected by the software. The software uses this and other morphokineticfeatures to differentiate multiple species in polymicrobial samples.

FIG. 93A illustrates the likelihood function for inference of thedistribution outcome for 19 microorganisms. CGL, Candida glabrata; KLE,Klebsiella spp. (K. pneumoniae, K. oxytoca, not differentiated); SPN,Streptococcus pneumoniae; STR, Streptococcus spp. (S. mitis, S.gallolyticus, S. agalactiae, S. pneumoniae, not differentiated); CAL,Candida albicans; PRO, Proteus spp. (Proteus vulgaris, Proteusmirabilis, not differentiated); CNS, coagulase negative Staphylococcus;EFS, Enterococcus faecalis; SMA, Serratia marcescens; ENT, Enterobacterspp. (Enterobacter aerogenes, Enterobacter cloacae, not differentiated);EFM, Enterococcus faecium and other Enterococcus spp., notdifferentiated, excluding Enterococcus faecalis; SAG, Streptococcusagalactiae; ECO, Escherichia coli; ABA, Acinetobacter baumannii; SLU,Streptococcus lugdunensis; SPY, Streptococcus pyogenes; CIT, Citrobacterspp. (Citrobacter freundii, Citrobacter koseri, not differentiated);PAE, Pseudomonas aeruginosa; SAU, Staphylococcus aureus; AO, acridineorange.

FIG. 93B is a flowchart of a method for identifying microorganisms.

FIG. 93C is a flowchart of another embodiment for identifyingmicroorganisms.

FIG. 94 illustrates the signal distribution data for the microorganismspresented in FIG. 93A, plotting target probe signal along the x-axis anduniversal probe signal along the y-axis. The dark dots represent signaland the light dots represent noise.

FIG. 95 illustrates the combination of five sets of Enterobacterdistribution experiments during instrument training in order to arriveat the Maximum Likelihood Estimation model unique for thismicroorganism.

FIG. 96 illustrates a test run showing a good case for the Enterobacterprobabilistic model.

FIG. 97 illustrates a test run showing a sparse case for theEnterobacter probabilistic model.

FIG. 98 illustrates a test run showing a compact case for theEnterobacter probabilistic model.

FIG. 99 illustrates a test run showing a poor case for the Enterobacterprobabilistic model, with most of the imaging emanating from noise.

FIG. 100 illustrates an updated version of the distribution expectationmodel. Additional microorganisms are included in the reference panelover the prior version shown in FIG. 94.

FIG. 101 depicts a generalized example of a suitable computingenvironment in which the described innovations may be implemented.

FIG. 102 is a flowchart of an example method of determining a targetdilution factor using dynamic dilution and diluting a sample using thetarget dilution.

FIG. 103 is a flowchart of an example method of determining a targetdilution factor for a sample using a growth factor.

DETAILED DESCRIPTION

The detailed description of exemplary embodiments herein makes referenceto the accompanying drawings, which show exemplary embodiments by way ofillustration and their best mode. While these exemplary embodiments aredescribed in sufficient detail to enable those skilled in the art topractice the inventions, it should be understood that other embodimentsmay be realized and that logical, chemical, and mechanical changes maybe made without departing from the spirit and scope of the inventions.Thus, the detailed description herein is presented for purposes ofillustration only and not of limitation. For example, the steps recitedin any of the method or process descriptions may be executed in anyorder and are not necessarily limited to the order presented.Furthermore, any reference to singular includes plural embodiments, andany reference to more than one component or step may include a singularembodiment or step. Also, any reference to attached, fixed, connected orthe like may include permanent, removable, temporary, partial, fulland/or any other possible attachment option. Additionally, any referenceto without contact (or similar phrases) may also include reduced contactor minimal contact.

As used herein, “AST” is antimicrobial susceptibility testing,antimicrobial agent susceptibility testing, or antibiotic susceptibilitytesting.

As used herein, “FISH” is fluorescence in situ hybridization.

As used herein, “EKC” is electrokinetic concentration. EKC is a processof applying an electrical field to microbial cells suspended in a fluidto produce migration of the cells toward the surface of a positiveelectrode.

As used herein, “GEF” is gel electrofiltration. GEF is a process ofsample preparation that relies on application of an electrical field tocause sample debris present in a sample to be separated frommicroorganism cells.

As used herein, “ID” is identification, such as a process of determiningthe species identity of a microorganism, for example, using FISH.

As used herein, “ITO” is indium tin oxide.

As used herein, an “LED” is a light emitting diode.

As used herein, “MHA” is Mueller Hinton Agar.

As used herein, “MLSb” is macrolide-lincosamide-streptogramin Bresistance.

As used herein, “MRS” is methicillin-resistant staphylococci.

As used herein, “MRSA” is methicillin-resistant Staphylococcus aureus.

Overview of Methods and System

Patient samples, such as blood samples, are the primary biologicalstarting point for assessing the etiology of a patient's disease anddetermining the appropriate therapy course for treating that disease.Key to reducing morbidity and mortality is initiating the propertherapeutic treatment of a critically ill patient at the appropriatedosage regimen as soon as possible. The historically weak link in thisprocess is sufficient cultivation of a microbial population to enableidentification of pathogen(s) present and to determine whichantimicrobial compounds the pathogen(s) will respond to in therapy.Reducing the assay time required to properly identify microorganism(s)in a patient sample and assess their drug sensitivity is crucial toimproving patient survival odds.

In many instances, patient samples contain multiple types ofmicroorganisms, such as mixtures of bacteria from differing genera,species, and even strains (also known as “polymicrobial” samples).Diagnostic accuracy is traditionally expressed in terms of sensitivityand specificity. Sensitivity refers to the probability of assigning adiagnostic test as positive when it is in fact, positive (the fractionof true positives), which confound the identification and antimicrobialsensitivity processes. The counter to sensitivity is specificity, whichis the rate of obtaining false negative test results. Current methods ofidentifying unknown microorganisms are prone to failure in both falsepositive and false negative modes. These difficulties with sensitivityand specificity are typically fostered by factors that impede sampledetection, such as noise, crosstalk, borderline resistance, and thelike. Traditional analysis methods often trade sensitivity of detectionfor the specificity of microorganism identification. In otherapplications, the reverse is true, prioritizing sensitivity overaccurate microorganism identification. But to maximize efficiency, andthus improve the odds of achieving a better treatment outcome for thepatient, both sensitivity and specificity need to be enhanced in balancewhen using a rapid, automated testing system.

Traditional methods for identification (ID) and antimicrobialsusceptibility testing (AST) of organisms from clinical specimenstypically require overnight subculturing to isolate individual speciesprior to biochemical assay-based identification, followed by growingisolated organisms in the presence of various antimicrobials todetermine susceptibilities. Molecular identification methods can provideorganism identification in a few hours directly from clinical specimensas well as resistance marker detection, but these methods do not providethe antimicrobial susceptibility information required by clinicians toinform treatment decisions. Studies demonstrating the feasibility ofusing various sample types including whole blood and respiratory sampleshave been reported, but sample preparation techniques require furtherrefinement. Current rapid molecular-based diagnostic methods only reportidentification and genotypic resistance marker results. While availablein a couple of hours, these results only provide a partial answer. Thisleaves the clinician to prescribe overly-broad spectrum empiric therapywhile waiting two to four days for conventional antibioticsusceptibility test results before adjusting therapy. The availabilityof an antimicrobial susceptibility test result in as few as 5 hours orless, as opposed to a few days, could potentially decrease morbidity andmortality in critically ill patients due to delays in administration ofappropriate therapy. In addition, rapid de-escalation frombroad-spectrum empiric therapies to targeted specific antimicrobialscould assist antimicrobial stewardship efforts to decrease the emergenceand spread of multi-drug resistant organisms (MDROs).

Rapid methods for identification and genotypic resistance markerdetection currently exist. However, absent a rapid method forantimicrobial susceptibility testing, clinicians lack fully actionableresults from such tests. Assays for the detection of additionalresistance phenotypes such as heterogeneous vancomycin-intermediate S.aureus (hVISA), extended-spectrum beta-lactamase (ESBL), and Klebsiellapneumoniae carbapenemase (KPC) have been reported. Nonetheless, suchprocesses known in the art are insufficient to efficiently andaccurately identify microbial populations in a patient sample—especiallypolymicrobial populations—particularly when the identification processis undertaken in a direct-from-sample manner.

To address these problems, massively multiplexed automated single celldigital microscopy was developed as a fully automated, microscopy-basedmethod that can in some embodiments perform bacterial/yeastidentification in about one (1) hour and antimicrobial susceptibilitytesting in about five (5) or fewer hours directly from clinicalspecimens. Various embodiments disclosed herein set forth a massivelymultiplexed automated single cell digital microscopy system employing aprocess that applies a Fluorescence In Situ Hybridization (FISH)-baseddetection protocol for the identification of microorganisms in patientsamples, whether the target sample is composed of a single, ormono-microbial population or a mixed, polymicrobial population. Toachieve this in an automated detection system, the system usessufficient information about typical or “expected” reference samplemicrobe populations (such as a reference panel of known microorganisms)in order to be “trained” to establish baseline expectations for typicalpatient samples subjected to FISH analysis. Once trained, the automatedsystem can apply the baseline parameters to evaluate a patient sample ofunknown composition with sufficient confidence that microbial cells canbe identified from sample contaminants. The relevance between unknownmicrobes in a sample and members of a reference panel is assessedstatistically. Thus, by application of a unique identification algorithmto FISH images, the identity of unknown microorganisms can be achievedwithout specific a priori knowledge of the type or types of organismspresent in a patient sample. And the process can be performed onmultiple samples loaded into a single disclosed instrument, makingmicrobial identification highly accurate and efficient.

The methods and systems described herein provide for microbialidentification through a process that combines FISH labeling of unknownmicrobes and analysis of that binding by Bayesian statisticalmethodology using probability distribution established by systemtraining. This approach enables better discrimination of microorganismsof interest from interfering noise and/or other microorganisms than isafforded by more traditional methodologies. These benefits are realizedwithout impairing detection sensitivity or specificity. This improvedtechnology avoids many of the pitfalls that beset “Frequentist” logic,which entails setting single a discriminatory value in order to minimizeType 1 errors (rejecting the null hypothesis when it is in fact true)and Type 2 errors (not rejecting the null hypothesis when in fact thealternate hypothesis is true). The methods and systems disclosed hereinprovide a process by which microorganisms may be discriminated and/oridentified by species and/or type. To achieve this end, variousembodiments utilize FISH as a tool for visualizing targetedmicroorganisms. Bayesian analysis is coupled with a model derived fromaccumulating an extensive data set to define thecharacteristics/descriptors to be employed in microbial identificationand sensitivity testing. The identity of unknown organisms is assessedin part by calculating a probability that determines the likelihood thatsequences in an unknown organism are complementary to FISH probesdirected to known reference organism sequences. The assessment includesassigning a posterior probability for one or more referencemicroorganisms given the observed probe binding of unknown organisms ina patient sample. This probability is Bayesian in nature, which assessesthe likelihood of the identity of the unknown microorganisms. Once themicrobial pathogens in a patient sample have been identified, the FISHresults (both counts and actual posterior probabilities) will steer ordirect AST sub-population analysis.

In aspects of the disclosure, an ID/AST System(identification/antimicrobial susceptibility testing) that providesidentification and susceptibility results in a few hours directly frompatient samples is provided. In some embodiments, the disclosed ID/ASTsystem provides clinicians information to make patient treatmentdecisions in a timely manner. In some embodiments, an ID/AST system asdescribed herein may aid antimicrobial stewardship efforts and lead toimproved patient outcomes.

In specific aspects of the disclosure, assays for detectingmethicillin-resistant Staphylococcus aureus (MRSA),methicillin-resistant staphylococci (MRS),macrolide-lincosamide-streptogramin b resistance (MLSb), and high-levelaminoglycoside resistance (HLAR) are provided.

System Overview

The system as disclosed herein may be exemplified in various embodimentsby Accelerate Diagnostics Instrument Model AD-1 (“AD-1”). The disclosedAD-1 instrument analyzes samples derived from various types ofbiological specimens, such as blood (or a fraction thereof, such asplasma or serum), respiratory samples (such as bronchoalveolar lavage,sputum, oropharyngeal swab, or nasopharyngeal swab), urine, etc., toperform identification (ID) of the species of bacteria present in thesample as well as to determine the susceptibility of the bacteria tovarious antibiotics from a panel of antibiotics (e.g., antibioticsusceptibility testing, or “AST”). The disclosed instrument isconfigured to perform these processes rapidly in an automated fashionthat does not require user intervention following preparation of thesample and initiation of an analytical run. The instrument comprises anouter housing and a base assembly. The base assembly supports a reagentstage, a cassette stage, a pipettor assembly for fluid handling, and anillumination and optics assembly for imaging the sample. An attachedsystem controller is configured to receive input and provide output, runthe instrument, and store and process image data acquired by the system.The system also comprises a reagent cartridge that contains all reagentsand consumables required by the instrument during a run as well as thecassette used to perform the ID and AST steps and facilitatemicroorganism visualization and image acquisition by the instrument'sillumination and optics assembly. In operation, a user prepares a sampleto be analyzed using the system and places a portion of the sample in asample vial provided as part of the reagent cartridge. The user placesthe sample vial containing the sample in position in the cartridge,inserts the cartridge into the reagent stage of the instrument, insertsa cassette into the cassette stage, and presses a button on theinstrument to initiate the ID and AST analyses, which the instrument andsystem can generally complete within 8 hours or less, such as less thanabout 5 hours (such as 4 to 8 hours, 4 to 6 hours, or 5 to 6 hours).

Each of the instrument components and the separate system components aswell as the method of operation of the instrument and system aredescribed in greater detail below.

Outer Enclosure and Base Assembly

Referring to FIG. 1, a perspective view of an instrument 100 isillustrated according to various embodiments. The instrument 100 maycomprise an upper enclosure 110, a lower enclosure 120, a door 130, anda base assembly 140. The upper enclosure 110 may enclose and protectmany of the instrument components, such as the illuminator, thepipettor, and the controller. The upper enclosure 110 may comprise arigid and lightweight material, such as plastic, aluminum, or acomposite material. The upper enclosure 110 may be generally rectangularwith rounded corners. However, those skilled in the art will appreciatethat the upper enclosure 110 may be any suitable shape. The upperenclosure 110 may comprise a flat top side 112.

The upper enclosure 110 may comprise an opening for a door 130. Invarious embodiments, the door 130 may be generally cylindrical orfrustoconical. The instrument 100 may comprise a button 150 which causesthe door 130 to open or close. In the illustrated embodiment, the button150 is located in a base assembly 140 of the instrument 100. However,the button 150 may be located at any suitable position on the instrument100. In response to a user pushing the button 150, the door 130 mayrotate to an open position.

Referring to FIG. 2, a perspective view of the instrument 100 with thedoor 130 in the open position is illustrated according to variousembodiments. The door 130 may rotate to open or close. The door 130 maybe driven by an electric motor. The door 130 may comprise a cut outsection 232 configured to receive a cartridge which contains reagentsand a specimen. A user may also insert a cassette through the open door130. A user may insert a cartridge and cassette through the cut outsection 232 of the door 130, and press the button 150 to close the door130. In various embodiments, pressing the button 150 to close the door130 automatically starts the ID/AST process.

Referring to FIG. 3, a perspective view of a back side 314 of the upperenclosure 110 is illustrated according to various embodiments. The backside 314 of the upper enclosure 110 may comprise an exhaust port 316.The exhaust port 316 may allow an exhaust fan 320 to expel air fromwithin the upper enclosure 110. The exhaust fan 320 may provide coolingto the instrument 100 as needed to maintain precise temperatures. Invarious embodiments, the upper enclosure 110 may comprise an insulationlayer configured to decrease heat escape from the interior of theinstrument 100.

Tight control of an instrument's enclosure temperature can be verydifficult to achieve using only an exhaust fan, particularly in view ofthe substantial variations in ambient laboratory temperatures that mayexist. Ambient laboratory temperatures may range, for example, fromabout 16° C. to about 32° C. depending upon geographic location,building heating and cooling controls, and other factors. Moreover, thepresence of one or more motors and/or heaters that exude heat in anenclosed instrument generally necessitates the use of a larger exhaustfan than otherwise would be needed for cooling the instrument. Inclusionof a larger exhaust fan necessitates enlargement of the overall size ofthe instrument, thereby increasing manufacturing costs. Even when aninstrument is adjusted to decrease its standard heat load by reducingheat-generating components to a set idle level when not in use, theinternal temperature of the instrument may not be sufficiently reducedto ensure that a proper temperature is maintained. Various embodimentsof the disclosed instrument provide a solution to this heat problem bydynamically adjusting the heat load of the instrument enclosure throughcontrol of waste heat emitted by components in the instrument itself.

In some embodiments, the instrument overcomes this heat problem bycontrolling its heat-generating components (e.g., motors and heaters)and—if desired—an exhaust fan with a proportional-integral-derivative(PID) controller algorithm. In other embodiments, only heat-generatingcomponents may be subject to dynamic control by the PID controlleralgorithm to alter the housing temperature of the instrument. Eachheat-generating component is independent, and the PID values for eachare added together to determine the overall heat control needed for theinstrument. In other words, an embodiment of the disclosure providesthat each heat-generating instrument component be a dynamicallycontrolled feature, such that the combination of PID algorithms adjustsfor the total heat output within the instrument housing. In essence,every component that generates heat during operation of the instrumentmay be subjected to PID algorithmic control. Thus, for example, wheneach motor in the instrument completes its task during a given samplerun, control of the motors may be relinquished in view of the enclosuretemperature. In this scenario, the activity of each motor may beincreased or decreased as needed to help control the overall instrumenthousing temperature. Anything that produces waste heat may be subject tothis type of control, even an instrument camera. Thus, to further refinethe waste heat control of the instrument, a camera may be manipulated toexude more or less heat as needed to control the temperature within theinstrument housing. This heat control factor is important, as theinstrument may be left on for days or weeks at a time, and the abilityto manipulate the waste heat from multiple components during idle timepermits finer control of the overall housing temperature. In doing so,instrument components will typically reduce their power usage when inidle mode, which improves the overall energy efficiency of theinstrument. Harnessing the waste heat of multiple instrument componentsin this fashion requires less cycling of motors and fans, therebyextending their useful lifespans. When exhaust fans reduce theircycling, less air intake occurs, which in turn reduces the amount ofdust that accumulates inside the instrument.

When each of the motors in the instrument completes its tasks, thesecomponents return to an idle power level, where they may remain untilcalled upon to function in a subsequent sample run. The instrument'smotors use more power when moving than when idle. However, idle modedoes not have to mean that power usage remain static at an arbitrarylevel. The instrument motors can change their power draw from about 25watts to as much as about 45 watts at idle to cool or warm theinstrument depending upon need in order to match a set point, forexample a set point of 31° C. A change of 20-25 watts is equivalent toabout a 100-125% increase in waste heat. In various embodiments, the PIDcontroller algorithm employs a power range extending from +100% to−100%. For each stepper motor (or any other heat-generating component),a minimum and a maximum idle torque are established which may then bescaled to fit the 0%-100% range for heat control. In variousembodiments, an exhaust fan of the instrument activates in the positiveregion of this range and the idle torque of a stepper motor activates inthe negative region of this range. Thus, for example, in the event of acool ambient laboratory temperature (when heating is required), the idletorque of a stepper motor activates to generate heat within instrumentuntil the set point temperature is reached. When a desired set pointtemperature of the instrument is exceeded, the exhaust fan activates andexpels heated air through an exhaust port.

This concept is further illustrated in FIG. 81A, which graphicallydepicts the results of a thermal stress challenge to the instrument inan environmental chamber simulating ambient laboratory temperaturesranging between 16° C. and 32° C. The graph plots instrument enclosurepower in direct current on the left y-axis against time in hours alongthe bottom x-axis. The right y-axis sets forth the instrument'senclosure temperature as measured and controlled throughout theexperiment using feedback from a thermistor. Following an initialbaseline equilibration period, a two-hour simulated assay was run ateach equilibrated temperature within the range, with the testtemperatures set forth in boxes in FIG. 81A. The enclosure temperatureof the instrument was stably maintained at a set point value of 31° C.when exposed to simulated laboratory environment temperatures from below18° C. to 30° C., as represented by the bottom line. Loss of enclosuretemperature control occurred at a simulated laboratory temperature of32° C., which is visualized by the upper line reaching and holding atthe 100% enclosure power mark. At simulated laboratory temperaturesbelow 22° C., the instrument enclosure temperature was controlled bymodulation of stepper motor idle torque via a PID algorithm. The coolerambient temperatures necessitated the generation of additional heat byincreasing the motor idle torque (negative enclosure power on thegraph), and retaining that heat by not running the exhaust fan of theinstrument in order to maintain the set point temperature of 31° C. Bycontrast, at simulated laboratory temperatures above 22° C., the samePID algorithm drove the expulsion of heated air by increasing the speedof the instrument's exhaust fan (positive enclosure power on the graph).As is evident from the graph, for most of the tested temperatures, theexhaust fan smoothly held the enclosure set point temperature when theambient temperature reached the threshold for reducing heat output byinstrument components and increasing removal of heated air via theexhaust fan. This paired dynamic activity eliminated the need forconstant switching on and off of the fan, as is observable ininstruments that experience far more dramatic temperature spikes. Thus,the instrument dynamically adjusts motor torque to maintain housingtemperature, thereby facilitating slower, more gradual temperaturechanges within a reasonable ambient temperature range. The overallresult is the ability of a smaller fan to perform exhausting functions,which permits the instrument to be constructed in a smaller housingspace.

By dynamically altering the waste heat generated by, for example, amotor's adjustable holding torque, fewer and/or smaller parts areincorporated into an instrument (e.g., fewer heaters and/or smallerfans), thus improving instrument reliability. Moreover, by harnessingwaste heat on an as-needed basis, power consumption of the instrumentdecreases in comparison to instruments that do not dynamically controlheat sources, thereby improving energy efficiency. Accordingly,exemplary embodiments of the instrument exercise control over enclosuretemperature while minimizing the consumption of electrical power.

Referring to FIG. 4, a perspective view of the instrument 100 upsidedown with the lower enclosure 120 separated from the base assembly 140is illustrated according to various embodiments. The lower enclosure 120may enclose and protect various components, such as various opticssystem components. The lower enclosure 120 may be coupled to a bottomside 442 of the base assembly 140. The lower enclosure 120 may comprisea plurality of apertures 422 configured to receive damping feet 444which support the instrument 100. The lower enclosure 120 may comprise arigid and lightweight material, such as plastic, aluminum, or acomposite material. The lower enclosure 120 may be generally rectangularwith rounded corners. However, those skilled in the art will appreciatethat the lower enclosure 120 may be any suitable shape. The lowerenclosure 120 and the upper enclosure 110 may be coupled to the baseassembly 140 via a plurality of screws 410. However, the lower enclosure120 and the upper enclosure 110 may be coupled to the base assembly 140by any of a variety of suitable attachment mechanisms.

A plurality of damping feet 444 may be coupled to the base assembly 140.The damping feet 444 may comprise rubber or a polymeric material whichdamps vibrations. The various instrument components, such as the reagentstage, cassette stage, optics system, pipettor, and illuminator may becoupled to the base assembly 140. Thus, the base assembly 140, reagentand cassette stages, optics system, pipettor, and illuminator may bevibrationally coupled, such that relative movement between componentsdue to external vibrations is limited.

The base assembly 140 may comprise a rectangular portion 446 and acircular portion 448. The pipettor, the illuminator, and the opticssystem may be coupled to the rectangular portion 446. A reagent stageand a cassette stage may be coupled to the circular portion 448. Thecircular portion 448 may be coaxial with the door 130. Thus, when thedoor 130 is open, a user may insert a cartridge and a cassette throughthe door 130 and onto the reagent stage and the cassette stage,respectively.

The base assembly 140 may comprise various connection ports 460. Forexample, the base assembly 140 may comprise a power input, acommunication port, and a camera output port. The connection ports 460may allow the instrument 100 to communicate with a computer or otherprocessor in order to receive commands and output data and otherinformation, such as image data or instrument status information. Invarious embodiments, the connection ports 460 may allow multipleinstruments to communicate with each other.

Reagent Stage

The instrument 100 may comprise a reagent stage which accepts auser-inserted reagent cartridge containing all reagents used duringoperation, including a sample. Referring to FIG. 2, the reagent stage500 is coupled to a top side of the base assembly 140. The reagent stage500 may be enclosed by the door 130. The reagent stage 500 is rotatableabout a central axis in order to move reagents within reach of thepipettor.

Referring to FIG. 5, a perspective view of the reagent stage 500 removedfrom the instrument 100 is illustrated according to various embodiments.The reagent stage 500 may comprise a generally annular shape with anouter circumference 510 and an inner circumference 520. A pair ofalignment walls 530 may be coupled to a top side 502 of the reagentstage 500. The alignment walls 530 may be configured to align acartridge when inserted into the reagent stage 500 by a user. Thealignment walls 530 may comprise curved guide surfaces 532 to direct thecartridge to the proper position as the cartridge is inserted. One ormore holding features 534 may be coupled to the alignment walls 530 forprecise alignment of the cartridge and to hold the cartridge in placerelative to the reagent stage 500. The holding features 534 may snap thecartridge in place when the cartridge is fully inserted to provide anaudible and physical indication to the user that the cartridge is fullyinserted. The alignment walls 530 may comprise one or more teachingwells 536. The teaching wells 536 may be a cylindrical depression in thealignment walls 530. The teaching wells 536 may be used by the pipettoras a reference point in conjunction with the cartridge in order to allowthe system to precisely locate all reagent wells in the cartridge.However, in various embodiments, the pipettor may use teaching wellsonly in the cartridge, only in the alignment walls, or in both thecartridge and the alignment walls.

The reagent stage 500 may comprise a plurality of GEF contacts 540. TheGEF contacts 540 may be spring contacts configured to engage pairs ofsliding contacts on the cartridge when the cartridge is inserted by theuser. The GEF contacts 540 may supply DC voltage to the sliding contactsin order to provide electrical power for the GEF process.

The reagent stage 500 may comprise one or more heating elements orheaters. The heaters may contact areas on the cartridge to maintainreagent temperatures. Each heater may comprise a flat plate with aprinted circuit type heating element bonded to its bottom surface. Aprecision thermistor may be embedded in the plate to provide feedbackfor temperature control. In various embodiments, the heaters maycomprise an antibiotic heater 550 and an agar heater 560. The antibioticheater 550 may be configured to heat an area beneath antibiotic wells inthe cartridge. The antibiotic heater 550 may extend between a portion ofthe inner circumference 520 and a portion of the outer circumference 510of the reagent stage 500. In various embodiments, the antibiotic heater550 may be configured to maintain the temperature of the antibioticwells to approximately 41° C. The agar heater 560 may be configured toheat an area beneath agar wells in the cartridge. The agar heater 560may be located adjacent to the inner circumference 520 of the reagentstage 500. Agar is a semisolid, and it may be desirable to melt the agarprior to use. The agar heater may heat the agar to above its meltingpoint (approximately 100° C.). Once the agar is melted, the agar heater560 may be configured to lower the temperature of the agar to just aboveits solidification temperature (approximately 50° C.).

In various embodiments, the reagent stage 500 may comprise a sensorwhich detects the presence or absence of a cartridge. For example, theGEF contacts 540 may be used to detect the presence of sliding contactson the cartridge. Additionally, the sensor may detect whether thecartridge was properly inserted.

Referring to FIG. 6, a perspective view of the bottom of the reagentstage 500 is illustrated according to various embodiments. The reagentstage 500 may comprise a platform 610 which is rotatable about a centralaxis. The rotation of the platform 610 may allow for the pipettor toaccess the sample and different reagents in the cartridge. The platform610 may be rotated by a stepper motor 620. The platform 610 may berotated using a spur gear 622 on a motor shaft 624 of the stepper motor620 and a ring gear 630 coupled to the platform 610.

Referring to FIG. 7, a section view of the reagent stage 500 isillustrated according to various embodiments. The platform 610 may rideon a bearing 710. In various embodiments, the bearing 710 may be a stagebearing. The platform 610 may rotate relative to the ring gear 630 and astator 720. A plurality of conductive rings 730 may be coupled to a topside of the stator 720. In various embodiments, the conductive rings 730may comprise nickel-plated copper. A rotor 740 may be coupled to abottom side of the platform 610. The rotor 740 may comprise a series ofspring loaded contacts 742. The spring loaded contacts 742 may maintaincontact with the conductive rings 730 as the platform 610 rotates. Powermay be applied to the heaters, thermistors, and GEF contacts, or anyother components coupled to the platform 610, via the spring loadedcontacts 742 and the conductive rings 730.

Cassette Stage

The instrument 100 may comprise a cassette stage 800. Referringtemporarily to FIG. 2, the cassette stage 800 may be coupled to the topside of the base assembly 140. The cassette stage 800 is configured toreceive a user-inserted cassette. The cassette stage may becircumscribed by the inner circumference of the reagent stage. Thecassette stage 800 is rotatable about a central axis and may alsotranslate in order to precisely locate the cassette with respect to theilluminator and optics system objective. In various embodiments, thecassette stage 800 may translate on a y-axis. In various embodiments,the rotation of at least one of the cassette stage 800, the reagentstage, and the door 130 may be coaxial.

Referring to FIG. 8A, a perspective view of the cassette stage 800removed from the instrument 100 is illustrated according to variousembodiments. The cassette stage 800 may comprise a cassette nest 810 anda heater plate 820. The cassette nest 810 may be generally annular anddefine a receiving well 812 configured to receive the cassette withinthe cassette nest 810. The heater plate 820 may define a bottom of thereceiving well 812. A user may insert the cassette into the receivingwell 812. The user may rotate the cassette to interface attachmentfeatures on the cassette with locating pins 814 on an inner wall 816 ofthe cassette nest 810. The interface between the attachment features andthe locating pins 814 may align and secure the cassette within thecassette nest 810.

Referring to FIG. 8B a cross-section view of a cassette 850 in thecassette nest 810 is illustrated according to various embodiments. Thecassette 850 may be supported by a glass ring 852 at an outer annulus ofthe bottom of the cassette 850. An air gap 860 may be present betweenthe heater plate 820 and the cassette 850.

Referring to FIG. 9, a section view of the cassette stage 800 showingheating components is illustrated according to various embodiments. Theheater plate 820 may be coaxial with the cassette. The heater plate 820may comprise a flat plate with a printed circuit type heating elementbonded to a bottom surface of the heater plate 820. A precisionthermistor may be embedded in the heater plate 820 to provide feedbackfor temperature monitoring. An infrared temperature sensor 830 may bemounted to the cassette stage 800 below the heater plate 820. Theinfrared temperature sensor 830 may be aimed through a viewing hole 822in the heater plate 820. The infrared temperature sensor 830 may beaimed at a bottom surface of the cassette in order to accurately measurethe temperature of the cassette. The temperature of the cassette may beused to approximate the temperature of the liquid in the channels.

Referring to FIG. 10, a section view of the cassette stage 800 showingrotational features is illustrated according to various embodiments. Thecassette stage 800 may rotate the cassette for pipetting, EKC, andimaging. The cassette nest 810 may ride in a bearing 1010. The cassettenest 810 may be rotated by a stepper motor 1020. The stepper motor 1020may use a spur gear 1022 on a motor shaft 1024 and a ring gear 1030 onthe cassette nest 810 to rotate the cassette nest 810. The stepper motor1020 may rotate the cassette nest 810 to align particular channels withthe pipettor or illuminator.

Referring to FIG. 11, a side view of the cassette stage 800 showingtranslational features is illustrated according to various embodiments.The cassette stage 800 may be mounted on one or more bearings 1010(illustrated in FIG. 10). In various embodiments, the bearings 1010 maycomprise “X-contact” ball bearings manufactured by Kaydon Corporation,Inc. (Muskegon, Mich.). However, in various embodiments, the bearings1010 may comprise cross roller bearings or any other suitable bearings.A linear actuator 1120 may comprise a lead screw 1122, a nut 1124 and astepper motor 1126. The linear actuator 1120 may drive the cassettestage 800 in the positive or negative y-direction. The linear actuator1120 may work in conjunction with the stepper motor 1020 which rotatesthe cassette stage 800 to position the cassette for pipetting, EKC, andimaging.

Pipettor

Referring to FIG. 12, a perspective view of an instrument 100 with theupper enclosure and the lower enclosure removed is illustrated accordingto various embodiments. The instrument 100 may comprise a pipettor 1200.The pipettor 1200 may be coupled to the rectangular portion 446 of thebase assembly 140. The pipettor 1200 may be cantilevered over thereagent stage 500 and the cassette stage 800 in order to access thecartridge and the cassette.

Referring to FIG. 13, a perspective view of the pipettor 1200 removedfrom the instrument is illustrated according to various embodiments. Thepipettor 1200 may comprise a fluid transfer pipette 1210 which uses apositive displacement air pump 1220 connected with a tube to adisposable pipette tip (not shown). The pipettor 1200 may comprise twomotor driven axes. A z-axis motor 1230 may drive a pipettor mandrel 1240in the positive or negative z-direction to raise or lower the pipettormandrel 1240. A theta-axis motor 1250 may rotate the pipettor mandrel1240 in the theta direction. The z-axis motor 1230 and the theta-axismotor 1250 may work in conjunction with the reagent stage motor and thecassette stage motors to accomplish the instrument's pipetting tasksduring ID and AST processes.

The pipettor 1200 may be used to determine the precise location andorientation of the cartridge after insertion. In some cases, thecartridge may not be inserted in exactly the same position each time.The pipettor 1200 may comprise a tip stripper collar 1260. The tipstripper collar 1260 may be substantially cylindrical. The tip strippercollar 1260 may be concentric around the pipettor mandrel 1240. The tipstripper collar 1260 may contact the edges of the teaching wells in thecartridge and/or the reagent stage. The instrument may calculate theposition of the cartridge based on the location of the edges of theteaching wells.

The pipettor mandrel 1240 may be the interface between the pipettor 1200and pipette tips included in the cartridge. The pipettor 1200 maycomprise a z-contact sensor and a tip presence sensor. The z-contactsensor may indicate that the z-axis drive has moved down (negativez-direction) without a corresponding movement of the pipette arm 1270.The tip presence sensor may indicate that the tip stripper collar 1260has been raised (positive z-direction) relative to the pipettor mandrel1240. The presence of a pipette tip is expected within a height range toindicate that a sealing portion of the pipette tip has been contacted bythe pipettor mandrel 1240. Once contact is made between the pipette tipand the pipettor mandrel 1240, the z-axis motor 1230 drives the pipettormandrel 1240 down. The downward motion may be translated to apredictable and repeatable sealing force through a lost motion spring's1280 spring constant. The tip presence sensor may indicate that thepipette tip is coupled to the pipettor mandrel 1240. In response to thetip presence sensor detecting the pipette tip earlier than expected ornot at all, the instrument may determine that the seal between thepipettor mandrel 1240 and the pipette tip is unsatisfactory, and thepipettor 1200 may remove the pipette tip and select a different pipettetip.

The pipettor 1200 may move in the theta and z-directions to removereagents or specimens from the cartridge. The pipettor 1200 may use apipette tip to pierce a film seal in a reagent well and obtain areagent. The pipettor 1200 may move to a desired input port in thecassette and form a seal between the pipette tip and the input port. Thepipettor 1200 may deposit the reagent into the input port and force thereagent into the sample channel. The pipettor 1200 may then remove thepipette tip and replace the pipette tip into the cartridge.

The pipettor 1200 may comprise a solenoid 1290 coupled to the theta-axisof the pipettor 1200. To remove a pipette tip, the solenoid 1290 mayextend and insert a removal pin 1292 into a removal receiver in the tipstripper collar 1260. Contact between the removal pin 1292 and theremoval receiver may prevent upward (positive z-direction) movement ofthe tip stripper collar 1260. The z-axis motor 1230 may lift thepipettor mandrel 1240 upward, and the pipette tip may contact the tipstripper collar 1260. The tip stripper collar 1260 may force the pipettetip away from the pipettor mandrel 1240, and the pipette tip may freefall into the cartridge. The pipettor 1200 may subsequently force thepipette tip downward in the event that the pipette tip does not fallcompletely into the desired position in the cartridge.

Illumination and Optics

A system may comprise an illumination system and an optics system. Anillumination system 1400 and optics system 1450 in accordance withvarious embodiments are illustrated in FIGS. 14-32. The illuminationsystem 1400 is primarily described with reference to FIGS. 14A-18E. Theoptics system 1450 is primarily described with reference to FIG. 14A andFIGS. 19-29. An illumination system 1400 (also referred to as an“illuminator”) can comprise a housing 1401, a stage configured toprovide movement of the illumination system 1400, one or more lightsources, mirrors, and condensers. In various embodiments, an illuminator1400 may also comprise an EKC electrode assembly. An optics system 1450can include an objective 1451, a focuser 1452, a fold mirror 1461, afocus LED 1453, a beam splitter 1462, a dual band filter 1454, a singleband filter changer 1466, a tube lens 1456, and a camera 1457. Each ofthese optics system components is described in greater detail below.

An illuminator 1400 in accordance with various embodiments providessample illumination for image acquisition. An illuminator 1400 may beconfigured to provide a plurality of light sources, such as white light1402 and laser diode light sources 1403, 1404. Each of the plurality oflight sources may provide illumination along a single optical path fromthe sample in cassette 1420 to the objective 1451 and camera 1457. Forexample, an illuminator 1400 may be configured to provide threeillumination sources to permit image acquisition of the sample incassette 1420, such as a green laser diode 1403 (excitationwavelength=520 nm) and a red laser diode 1404 (excitation wavelength=637nm) for illumination used during the ID (FISH) process and a white lightLED 1402 for dark field illumination (the “dark field LED”) used duringboth the ID and AST processes. An illuminator 1400 comprising aplurality of light sources having the same optical path can facilitateoverlaying images containing the same image features (i.e., a field ofview containing the same sample objects at the same sample objectlocations within the field of view) acquired using differentillumination, such as overlaying a dark field image of a field of viewwith green and red fluorescence imaging to determine which imagefeatures are debris (i.e., not marked with a hybridized FISH probe) andwhich are microorganism cells.

In various embodiments, the illuminator 1400 can comprise an aluminumcylindrical tube housing 1401, the red 1404 and green 1403 laser diodes,and the dark field LED 1402. The illuminator housing 1401 may be mountedto the base assembly 140 by an illuminator stage configured to providez-axis and theta-axis movement suitable to position the illuminator 1400directly above the cassette and in coaxial alignment with the objective1451. Z-axis movement permits the laser 1403, 1404 and dark field 1402light sources to be positioned at different vertical positions above thecassette to enable proper focusing of the light emitted by the lightsource relative to the sample contained in a cassette sample channelflowcell. Z-axis movement together with theta-axis movement allowsrotation of the illuminator 1400 away from the area above the cassettepermitting pipettor access to the cassette.

The green 1403 and red 1404 laser diodes can be mounted on the outsideof the aluminum illuminator housing 1401 described above. The lasers1403 and 1404 may be aimed downward and reflected off a 45 degreediagonal mirror 1405 mounted midway along the housing 1401. The laserlight passes through a hole 1406 in the side of the housing 1401 and isreflected downward off another 45 degree diagonal mirror 1407 (locatedbelow the optical stop 1408, described below) so that the light iscoincident with the axis of the objective 1451 (i.e., coaxial with theobjective 1451).

White light from the dark field LED 1402 exits the LED and passesthrough an upper collimating lens 1409 mounted at the top of theilluminator 1400 just below the dark field LED 1402. The uppercollimating lens 1409 shapes the light into a cylinder. Light in thecenter of the housing 1401, 20 mm in diameter, is blocked with anoptical stop 1408. At the bottom of the housing 1401 a lower condenserlens 1410 re-shapes the light into a cone with a focal point suitablefor dark field illumination.

In various embodiments, an illuminator subassembly may be configuredwith an EKC electrode assembly. For example, as shown in FIG. 15, an EKCelectrode assembly 1430 can comprise two spring-loaded, rhodium-platedpins that are mounted in a non-conductive (polymer) block on the side ofthe illuminator 1400. The system may perform an EKC procedure bypositioning the illuminator so that the EKC-electrode assembly pins arereceived by corresponding EKC-electrode ports in the cassette andcontact electrodes located in the EKC-electrode ports. A first pin 1431contacts an electrode located on and operatively connected to the topsurface of the glass layer of the cassette. A second pin 1432 contactsan electrode located on and operatively connected to the lower surfaceof the molded plastic upper housing of the cassette. The EKC electrodeassembly 1430 may be used to deliver an electrical potential to thesample channels in the cassette. For example, a low voltage (0.6 to 1.6volts DC) may be applied to the cassette sample channels by theinstrument using the EKC electrode assembly 1430 located on theilluminator subassembly 1400. The electrical potential may be appliedfor a defined period of time, such as several minutes, as described ingreater detail elsewhere herein.

The sample cassette 1420 placed in the system by a user may bepositioned by the cassette stage so that a sample channel (or flowcell)is located just below the illuminator 1400 and just above the objective1451 (i.e., so that a portion of the sample channel comprising anobjective field-of-view is positioned in-line between the illuminator1400 and the objective 1451).

In accordance with various embodiments, an optics system 1450 maycomprise an objective 1451. The objective 1451 may be mounted to thebase assembly 140 and located below the base assembly 140 such that theobjective 1451 is positioned beneath the cassette stage. An objective1451 can comprise a standard “off the shelf” microscope objectivesuitable to collimate an image. For example, in various embodiments, anobjective with a numerical aperture of 0.45, a field number of 22 mm,and a magnification of 20× may be used. Objectives with otherspecifications may be used in accordance with various embodiments of thepresent disclosure.

The objective 1451 may be mounted to a focuser mechanism 1452. Thefocuser mechanism 1452 may be configured to move the objective 1451along a z-axis (i.e., substantially normal to the horizontally-orientedsample channel holding the sample) to focus the image. In variousembodiments, the objective 1451 may be moved in the z-axis by means of astepper motor with a precision cam mounted on its shaft. The cam cancomprise a round disk with an offset mounting hole. A stepper motor witha microstepping mode may be used to drive the cam. The stepper motor mayprovide, for example, a 200 step/revolution movement in standard mode,with a microstepping mode yielding 25,600 microsteps per revolution toprovide sub-micron resolution movement of the objective 1451.

The optics system 1450 may further comprise a fold mirror 1461. The foldmirror 1461 may be used to fold the optical path 90 degrees to redirectthe optical path from the z-axis (i.e., coaxial to the illuminator 1400and objective 1451) to the y-axis direction beneath the base plate andtoward a tube lens 1456 and camera 1457 used for image acquisition,described below.

The optics system 1450 may comprise a focus light system. A focus lightsystem can comprise a focus LED 1453 and a beam splitter 1462. Invarious embodiments, a focus LED 1453 may be used to provide a separatelight source that is reflected off the top (glass) surface of thecassette to facilitate focusing the objective 1451. The focus LED 1453,a pinhole aperture 1463, and a collimating lens may be positioned at aright angle to the main optical path. A beam splitter 1462 can be placedin the optical path and used to fold the focusing light source into theoptical path. The focus light system may be used in conjunction with arapid focus method performed by the system in response to directionsprovided by the controller, wherein the rapid focus method is configuredto acquire and process image information based on focus lightillumination reflected off of the cassette 1420 and to adjust theposition of the objective 1451 accordingly.

The optics system 1450 may comprise a field stop 1464. The field stop1464 may comprise a circular aperture 1465 configured to block straylight from reaching the camera 1457.

The optics system 1450 may further comprise a filter or a plurality offilters. The filter or plurality of filters may be placed in a fixedposition in the optical path. The filter or plurality of filters may beconfigured to block one or more wavelength of light, such as wavelengthsof light used for fluorescence excitation (e.g., the red laser diodeexcitation wavelength 637 nm) and pass dark field light and emissionwavelengths (e.g., a 669 nm red wavelength and a 553 nm greenwavelength) used for a FISH ID procedure.

The optics system may further comprise a single band filter 1455. Thesingle band filter 1455 may be located on a single band filter changer1466. The single band filter changer 1466 may be configured toreversibly interpose the single band filter 1455 in the light path topermit the emission green wavelength (553 nm) and block all otherwavelengths during the FISH ID procedure. The single band filter changer1466 may use a stepper motor to move the single band filter 1455 in orout of the optical path. The single band filter 1455 is introduced intothe path only when imaging the emitted green light (553 nm).

Other filter configurations may be used in accordance with variousembodiments. For example, an optics system can comprise any suitablenumber of filters, including a plurality of reversibly interposablefilters. The filters may be selected based on the combinations of lightsources and fluorophores used, and various combinations of lightsources, fluorophores, and stationary and movable filters are possibleand within the scope of the present disclosure.

The optics system may further comprise a tube lens 1456. The tube lens1456 can be a multi-element lens configured to reimage light in theoptical path onto a camera sensor surface. In conjunction with theobjective 1451, the optics system 1450 provides a 6.1-fold magnificationof the field of view (i.e., a 20× magnification objective and a 0.305×magnification tube lens). In some embodiments, the optics systemcomprises a telecentric lens providing orthographic projection, therebypermitting measurements to be taken without scaling, although other lenstypes may be suitable.

The optics system 1450 may further comprise an image sensor (notseparately shown, but embedded within the camera 1457). An image sensormay be used to acquire images of the sample. In various embodiments, animage sensor can comprise a complementary metal-oxide semiconductor(“CMOS”) sensor or active pixel sensor camera. For example, a CMOSsensor camera can be a five megapixel grayscale camera used to acquireimages for processing. In various embodiments, a passive pixel sensorcamera, such as a charge-coupled device image sensor, may be used. Inaccordance with various embodiments, an image sensor is operativelyconnected to a controller and transmits acquired images to thecontroller for storage and data processing.

Automated high speed microscopy requires a system configured to rapidlyadjust the relative distance between a microscope objective lens and asample in order to arrive at a correct distance between the two.Establishing this correct distance ensures that sample features ofinterest are imaged in the proper focal plane for accurate sampleanalysis. The optics system 1450 of the present disclosure may beconfigured to enable very rapid focusing of a series of successiveimages (frames) of microorganisms in a given sample during an analyticalrun of the instrument. To achieve this, the instrument undertakes amethod for quickly focusing the optics system 1450 on an object ofinterest, (e.g., a microorganism) in less than about 500 ms. The opticssystem 1450 can establish a focal plane with as few as only two trialimages coupled with a learning algorithm that may allow as few as twotest images to be used in this process. Image data can be storeddigitally and compared over time for changes in one or more samplefeatures. Not only does the disclosed process provide a very fast methodfor rapid focusing in a series of successive frames captured at aspecific location in a field of view, the process does not requireexpensive or specially manufactured components to be accomplished.

Fast Focusing of Optical System

Unlike focus methods known in the art (e.g., “hunting and estimation”algorithms), the optics system 1450 may employ a rapid focusingalgorithm to mathematically calculate the true focus position for anindividual microorganism or an individual colony in a sample, asillustrated in FIGS. 76-80B and FIGS. 82A-82C. One skilled in the artwill recognize that this disclosed fast focusing method can be used withother systems and methods (such as those used to detect cells ormicroorganisms), in addition to those disclosed herein. Control ofmovement of the objective and processing of the algorithms describedbelow can be accomplished using hardware components shown in FIG. 101,which can be imbedded on the control board 321. Movement of theobjective causes defocus of a reflected point source image on both sidesof the image plane for a sample feature of interest (e.g., a microbe),as shown in FIGS. 78 and 79. The rapid focusing algorithm derives avirtual true focus position (pt) by acquiring two focus data points p1and p2, which lie on either side of the true focus position delineatedby plotting point source spot areas in pixels on the x-coordinateagainst the positions of the instrument's objective, as set forth inFIGS. 77, 80A and 80B. The true focus point pt is the local minimum ofthe curve created by computing the linear proportional position betweenp1 and p2. Optimally, the best image will have the smallest spot whichis nearly the same as the diameter of pinhole 1463 (e.g., about 25microns). To this end, the instrument may comprise software that directsobjective 1451 through the following algorithmic steps:

-   -   Move objective to an out of focus—far position;    -   Acquire image of point source spot;    -   Move objective to out-of-focus—near position;    -   Acquire image of point source spot;    -   Use center of spot locations and spot sizes to compute correct        focus position for objective;    -   Move objective to computed location and acquire actual image.

The center spot locations can be computed for a grayscale focus image,the content of which can be irregular and of varying intensities.Knowing the center of the focus image spot provides the focusingalgorithm with the directional components required for fast focusing.Pixels in the image are indexed by row and column, and have intensityvalues from 0-4095 (12 bit grayscale) in one implementation. All pixelsin the image are examined. Each pixel intensity value is multiplied byits row and added to a ‘row sum’. Each pixel intensity value ismultiplied by its column and added to a ‘column sum’. Also, each pixelintensity value is added to a ‘full image sum’.

Once all pixels have been examined and the sums accumulated, theintensity weighted center row and column of the image centroid arecomputed as:

Row sum/Full image sum

Column sum/Full image sum

Using this method, brighter pixels contribute more weight to thecomputed position, while darker pixels contribute less. Thus, theresults represent a good measure of the center position of the imagecontent.

In some embodiments, the order of the steps may be altered for thislearning algorithm (by which the instrument learns or identifies thespecific location in the field of view of a given microorganism). Forexample movement of the objective to the out-of-focus—near position andimaging of the near point source spot may be performed before theobjective is moved to an out-of-focus far position and imaging of thefar point source spot is performed. Site-to-site variations inreflectance and other such parameters may change the absolute values ofthe focal curve (e.g., raising or lowering the curve along the x-axis)but this type of variation does not alter the resultant computed truefocus position. The initial pass of focusing at a given site can takemore than two focus images before two “qualified” focus images areachieved and true focus can be computed. During the first imaging pass,the learning algorithm uses neighboring sites that are close to the sameheight as initial starting points for focus trial images. Duringsuccessive passes, the learning algorithm uses the previous pass focustrial image positions for the site to guide the focus trial images. Inall cases, the focus trial images must not be “too near” or “too far”away from the true focus point. If they are, then new focus trial imagesare taken. Mathematically, the true focus position can be identified asfollows.

$\begin{matrix}{\frac{x\; 1}{a\; 1} = \frac{x\; 2}{a\; 2}} & {{Equation}\mspace{14mu} 1} \\{or} & \; \\{\frac{x\; 1}{x\; 2} = \frac{a\; 1}{a\; 2}} & \; \\{{{p\; 2} - {p\; 1}} = {{{x\; 1} + {x\; 2\mspace{14mu}{or}\mspace{14mu} x\; 1}} = {{p\; 2} - {p\; 1} - {x\; 2}}}} & {{Equation}\mspace{14mu} 2} \\{p_{t} = {{p\; 2} - {x\; 2}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Substituting x1 from Equation 2 into x1 of Equation 1:

$\frac{{p\; 2} - {p\; 1} - {x\; 2}}{x\; 2} = \frac{a\; 1}{a\; 2}$${\frac{{p\; 2} - {p\; 1}}{x\; 2} - 1} = \frac{a\; 1}{a\; 2}$$\frac{{p\; 2} - {p\; 1}}{x\; 2} = {\frac{a\; 1}{a\; 2} + 1}$${{p\; 2} - {p\; 1}} = {\left( {\frac{a\; 1}{a\; 2} + 1} \right){x2}}$${{x\; 2} = {\frac{{p\; 2} - {p\; 1}}{\frac{a\; 1}{a\; 2} + 1} =}}\mspace{11mu}$

Substituting x2 from the above into Equation 3 yields final equation:

$p = {{p\; 2} - \frac{{p\; 2} - {p\; 1}}{\frac{a\; 1}{a\; 2} + 1}}$

Using these equations, for example, if:

-   -   p1=4.581, a1=1819, p2=5.823 and a2=1623 then, the resultant        pt=5.237.

A prescan 8000 is performed in FIG. 80C in order to obtain the graphsshown in FIGS. 80A and 80B. In process block 8002, a coarse focusdiscovery is initiated. The focus discovery is accomplished in twopasses: a coarse focus discovery pass 8002 and then a fine focusdiscovery pass 8004. The coarse focus discovery pass is used to locate aposition of the light source (e.g., LED). The focus LED appears in thecamera image in a different location for every instrument, and if aninstrument has been serviced, it may change. Thus, during the coarsefocus discovery pass, a location of a reflection generated by the lightsource needs to be determined. To accomplish this, full-screen images(e.g., 2592×1944 pixels) are taken during the coarse pass. The coarsepass is repeated for multiple iterations of different focus positions,such as different focus positions between 100 to 500 microns (50 micronincrements, for example). At process block 8006, the coarse focusdiscovery is started. In process block 8008, the objective is moved to aposition of 100 microns (though one will recognize that other startingpositions can be used). In process block 8010, a full screen image iscaptured. In process block 8012, a weighted spot area and spot offsetare determined using the techniques described herein. Computing theweighted spot area involves computing the area for just a single image.The weighted spot area is computed using grey-scale values (lightintensity) for the pixels and relative row and column position of thepixels. The spot offset is calculated as an offset from image center. Indecision block 8014, a check is made whether the focus position hasreached a predetermined distance (e.g., 500 microns, in this example,but other distances can be used). If not, then in process block 8016,the next focus position is computed, and process blocks 8010 and 8012are repeated so that the image is re-captured. Once decision block 8014is answered in the affirmative, then in process block 8018, the capturedimage spot areas are analyzed. Analyzing the captured image spot areasinvolves using the computed weighted spot areas of all of the imagestogether (the curve), and looking for the best focus position. The spotareas are used to find the curve bottom. Once the bottom is found, fixeddelta positions on either side of the best focus position are used tolookup the spot offset ranges to later use when focusing.

FIG. 77 shows a portion of an M-shaped graph that can be obtainedthrough performance of process blocks 8010, 8012, and 8018. The figureshows the M-shaped curve in a trough between the two legs (not shown) ofthe “M.” A good focus position is at the valley at the center of theM-shaped curve. During the coarse focus discovery phase a determinationis made whether the spot moves left or if it moves right when the focusposition increases. The algorithm learns the direction of movement anduses such direction for future calculations.

At decision block 8020, a check is made whether the proper LED positionwas found and the trough in the M-shaped curve. If not, then at processblock 8022, the coarse focus discovery terminates. An error can bereported to the user interface. On the other hand, if decision block8020 is answered in the affirmative, then in process block 8030, a goodfocus position can be determined using the bottom-most point of theM-shaped curve in the area of the trough.

This coarse phase 8002 results in targeting where to perform the finefocus discovery pass 8004. The fine focus discovery pass uses much finerfocus position steps, centered around the best coarse focus position,and uses smaller (e.g., 401×401 pixels) images, because it is known atwhich part of the image the LED appears.

Fine focus discovery 8004 starts at process block 8040. In process block8042, the focuser (objective) is moved to a first position. In processblock 8046, a spot can be displayed on the image from a light source anda camera can be used to capture the spot. In process block 8048,intensity weightings and spot offset are computed in a similar fashionas was described above. In decision block 8050, a check is made whetherthe focus position is greater than a last focus position, which is apredetermined number. If not, then in process block 8052, a next focusposition is computed, such as by adding a predetermined distance to thefocus position (e.g., 10 microns). If decision block 8050 is answered inthe affirmative, then in process block 8054, the captured image spotareas are analyzed. For example, the spot areas can be computed, such asby measuring a distance across the spot. Additionally, an offset fromthe center of the image can be computed. In process block 8056, aminimum spot area is determined. The minimum spot area is associatedwith the best focus position. In process block 8058, left and rightoffset ranges are determined for the autofocus feature. The rangesdictate whether a captured image of a spot is acceptable or not. Oncethe ranges are chosen, then the prescan phase ends in process block8060.

With the prescan completed, autofocus can be accomplished using aslittle as two images. Generally, an image is captured with the focusposition in front of and behind the image plane by predeterminedamounts. Assuming that the spot is within the spot offset rangedetermined during the prescan, an intensity weighting can be computedfor each spot and mapped onto the prescan data to determine the bestfocus position for the objective. The overall decision path followed bythe instrument can be visualized in the process flow depicted in FIG.82A. The process begins at process block 8202. At process block 8204, afirst image is captured or acquired for a first curve side. An attemptby the system is made to acquire a focus image for an object of interest(e.g., a microorganism) on one side of the focus point curve (the firstside). Accordingly, a predetermined focus position (P1, FIG. 77) is usedwherein the objective is moved in order to estimate a first spot offset.Using that positional information, the objective 1451 is moved so thatthe focus point is a predetermined distance in front of the image plane.For example, in FIG. 76, an image plane 7620 that includes aglass/liquid interface (i.e., the microorganism positioned upon a glasssubstrate) is shown as being the best focus position for the objective1451. However, the objective 1451 is moved to the left, as shown byarrow 7640 so as to change the focus point to a predetermined distancein front of the image plane 7620. Consequently, the focus point ischanged to a position shown at 7610, purposely a predetermined distancein front of the image plane. If the image initially obtained from thefirst side of the curve is outside of the required range, then theinstrument makes slight parameter adjustments and acquires an image ofthat position again. Thus, in decision block 8206 of FIG. 82A, a checkis made whether the image is within the required range. Specifically, arange was determined in process block 8058 of FIG. 80C during theprescan. That range relates to spot offset versus focus positioninformation obtained during the prescan and shown in FIG. 80B. A spotoffset is an offset of the center of the spot from dead center, which isa known center. For example, in FIG. 78, a known center of the image isshown at 7810. The center of the spot is shown at 7820 and is calculatedby measuring from one point on the edge of the spot to an opposite edgeat several locations. The offset is a distance between the center of theimage 7810 and the center of the spot 7820. That distance is used indecision block 8206 to determine whether the captured image includes aspot that is within the required range. If decision block 8206 isanswered in the negative, then in decision block 8208, a check is madewhether the number of attempts made to capture the image exceeds athreshold. If so, then a failure occurs in process block 8210.Otherwise, a position of the objective is changed in a directionnecessary to move the spot offset within the desired range in processblock 8212. The proper direction can be determined using the prescanspot offset data of FIG. 80B. After the objective is modified in processblock 8212, an image of the spot can be reacquired. Thus, the processincludes repeatedly moving the objective and re-capturing the firstimage until the offset distance is within the predetermined limits. Ifthis image is within the required range, then the same procedure isfollowed on the opposite (second) side of the curve in process block8220. Thus, the objective position is moved to point P2 (FIG. 77). Thefocus point is thus changed to a new out-of-focus position 7630 (FIG.76) at a distance behind the image plane, as shown at 7630. In processblock 8222, an image is acquired for the newly selected out-of-focusposition.

Process blocks 8224, 8226, 8228, and 8230, are similar to thosedescribed above for the first image acquisition and will not bere-described for purposes of brevity. The true focus position iscalculated from the out-of-focus images from the first and second sidesof the focus point curve in process block 8250. In order to calculatethe true focus position, the prescan curve of FIG. 77 can be used. Anintensity weighting is determined for each image and mapped onto theprescan information as shown at points Q1 and Q2 (FIG. 77). Theintensity weighting can be determined by taking a 12 bit grayscale imageof the reflected light from the focus LED, and computing arepresentative spot area. Each pixel in the image has an intensity valueof between 0-4095. The computing of the spot area is determined knowingthat actual focus images can be quite bright or quite dim, so analgorithm that operates on it should be insensitive to actual imagebrightness. To accomplish that, first the average pixel value for allpixels in the image is computed. Then, a pixel intensity threshold iscomputed by taking 80% of that average. All pixels meeting or exceedingthat pixel intensity threshold are counted to produce the spot area. Theresultant spot area is thus insensitive to absolute image intensity.

A determination can then be made where the bottom of the parabola islocated and the objective position associated with the bottom of theparabola is used as the true focus position. While the true focusposition is fairly accurate, in process block 8252, a visual focusposition is calculated by adding a predetermined offset (a known offset)to the true focus position. The visual focus position is merely acalibration adjustment to take into account the particular application,which is focusing for capturing images of microorganisms (e.g., bacteriaor fungi). Once the offset is applied, the objective is moved to thecomputed position for imaging the microorganism. In process block 8260,an image is captured of the microorganism.

Thus, the visual focus position is computed and the instrument employsthis information to move to the true focus position of the microorganismof interest and then acquires an image. If during the learning process aparticular image is unable to be obtained in the required range within acertain number of attempts (for example, 20 attempts), then theinstrument will deem that position to be unfocusable, abandon the imagelocation, and move on to another object of interest to begin the processanew. In some embodiments, two or slightly more than two—but rarely morethan five—focus images may be required to establish the true focus pointof an image. The instrument utilizes offset spots (pixels of offset) toset zones on either side of a focus point that permit mathematicalcalculation of the true focus point. After the initial pass at the firstsite, the instrument “learns” from the initial data and may reduce thenumber of images needed for calculation of the true focus point to onlytwo per site at additional locations in the field of view. This learningprocess substantially reduces the amount of time needed to focus uponand repeatedly capture images of multiple microorganisms in amicrofluidic sample channel 5702. The ability to mathematicallycalculate the precise location of the true focus point of anobject—without the instrument's objective having to actually go to thatsite to home in on the location by trial and error—is desirable toobtaining images in a fast, accurate manner.

Once a true focus position is located for a particular microorganism,the instrument will store location data for that microorganism, therebypermitting the instrument to move on to the next microorganism in asample within a microfluidic sample channel 5702 of the cassette 4500and repeat the process, thus imaging this next microorganism. Theinstrument may retrieve stored image location data and repeatedly returnto a specific microorganism in a given field of view to capture multipleimages over time, thereby memorializing any responses or changes themicroorganism exhibits under a test condition (e.g., growth in thepresence or absence of an antibiotic). The rapidity with which thisstep-wise determination of the true focus position of a microbe in abiological sample is achieved is key to facilitating within a matter ofa few hours (preferably less than eight hours, more preferably less thanseven hours, and even more preferably less than four hours, such as 3-8hours, 4-8 hours, 4-7 hours, or 4-6 hours) the identification ofmicroorganisms in the sample as well as their sensitivity to treatments,such as the sensitivity of bacteria to particular antibiotics.

In some embodiments, a light source 1453 illuminates a sample. Objective1451 is moved through the z-plane to locate the best image locale. Onceidentified, the targeted reflective spot may be revisited multipletimes, the first time establishing a baseline against which additionalimages may be compared for changes or modifications. The z-height offocuser 1452 does not change much from site-to-site on a given glasssupport, although there will be slight variations due to imperfectionsintroduced to the glass support during manufacture. Thus, initial dataobtained from a first image pass can be used to inform the instrumentabout where to target a second site on glass support 4400 for imaging asecond object (microorganism) of interest.

Because microorganisms are three-dimensional entities, and because thecontours of a microorganism may change with growth or exposure toenvironmental challenges (temperature alterations, nutritionalconditions, antibiotics, etc.) during an analysis period, objective 1451targets the bottom or glass contacting area of a microorganism forconsistency in measurement over time. By contrast, focusing on the topof a microorganism during baseline measurement would make it difficultto locate the exact same spot some minutes to hours later after themicroorganism has grown in size or multiplied. Under this scenario, thedepth of the original focal spot would differ at each return visit bythe objective due to additional cell mass, introducing the potential forerror in measuring from the originally targeted position. The rapidfocus algorithm described herein overcomes this deficiency.

In certain embodiments, microorganisms are immobilized in a medium (suchas one including agar or agarose) and therefore remain in the samelocation during the analysis time period, permitting repeated imagingvia the bottom or glass contacting area of a microorganism which thetracks features that change in response to changes in environmentalconditions. For example, the microorganism can be retained on a surfaceof the support (such as a flowcell or glass support), thereby producinga retained microorganism.

In certain embodiments, camera 1457 communicates with a systemcontroller, which may be a host computer. The system controllercomprises software that instructs instrument 100 where to move focuser1452. The system controller instructs LED light source 1453 to turn onand camera 1457 to capture an image. In some examples, no image data isstored in the instrument module; image data is stored in—and laterretrieved from—the system controller.

FIG. 82B is a flowchart according to one embodiment for autofocusing ona microorganism. In process block 8266, prescan information is providedthat includes intensity weightings versus focus position. The prescanflow was described in FIG. 80C, above. During the prescan, a pluralityof intensity weighting versus focus position information is obtained fora range of different focus positions. The results can be plotted asshown in FIG. 80A. Likewise, spot offset versus focus position can beobtained in some embodiments over a plurality of focus points as shownin FIG. 80B. In process block 8268, a light can be projected from alight source through the objective at an image plane. For example, thelight source can be a first light source designed to project a beam oflight that generates a reflection off of a glass/liquid interface.Example light sources include an LED, laser diode, etc. (such as diode1403 or 1404). The light source can be a colored light source so thatthe reflection is easily visible. However, using greyscale analysis,greyscale values are used to measure the reflection, so colored light isnot required. The light source used to generate the reflection is adifferent light source than that used for imaging the microorganism. Inprocess block 8270, a first image is captured of the reflected lightwith the objective (e.g., 1451) at a first position. The first positionis an intentionally out-of-focus position with a focal point in front ofthe true focal point. An example of such an out-of-focus position isshown by focus point 7610 in FIG. 76. In process block 8272, a secondimage is captured of reflective light with the objective at a secondposition. In this case, the second position is an intentionallyout-of-focus position behind the true focal point. An example of such anout-of-focus position is shown by focus point 7630 in FIG. 76. Inprocess block 8274, intensity weightings are computed for the reflectedlight of the first and second images. As described above, the intensityweightings take into account the greyscale values and row/columninformation to provide a weighting to each pixel in the image. Inprocess block 8276, a focus position is calculated using the computedintensity weightings in association with the prescan information. Thecomputed intensity weighting can be mapped onto the prescan informationand a lowest point (lowest intensity weighting) of the prescaninformation can be determined. The focus position associated with thatdetermined point is the desired focus position (although some additionalcalibration may be required). In process block 8278, the objective ismoved to the calculated focus position. At this point, the focusposition should be the true focus position on the image plane, as shownat 7620 in FIG. 76. In process block, 8280, with the objective properlypositioned, an image of the microorganism can be captured.

Using the process described above, the autofocus feature can beaccomplished in as little as two images, making it faster than othersolutions.

FIG. 82C shows a flowchart according to another embodiment. In processblock 8282, an objective (e.g., 1451) is moved to a first position sothat a focus point is in front of an image plane. The image plane (e.g.,7620) is typically a glass substrate with a microorganism-based liquidthereon. By “in front” of the image plane, it is meant that the focuspoint is in-between the image plane and the objective. Thus, theobjective is moved in a direction away from the image plane to make thefocus position intentionally out-of-focus. By so doing, a point Q1 (FIG.77) can be determined and mapped onto an existing graph obtained duringthe prescan phase. In process block 8284, a first spot-shaped reflectionis generated using the objective at the out-of-focus position. A firstlight source, such as diode 1403 or 1404, can be used. Based on theobjective position, different greyscale values and offsets will begenerated. In process block 8286, a first image is captured of the firstspot-shaped reflection. For example, the camera 1457 is used to capturethe first image under direction of the controller. In process block8288, the objective is moved to a second position (see P2, FIG. 77) sothat a focus point is a predetermined distance behind the image plane.By being behind the image plane it is meant that the image plane isin-between the focus point and the objective. In process block 8290, thefirst light source is projected through the objective to generate asecond spot-shaped reflection. By “first” light source, it is meant afirst type of light source. Thus, laser diodes 1403 and 1404 are bothconsidered a first light source as they are used for generatingreflections. In process block 8292, a second image is captured of thesecond spot-shaped reflection. In process block 8294, an intensityweighting analysis is performed on the first and second images. Theintensity weighting analysis is used to determine a focus position ofthe objective. For example, the intensity weighting values Q1 and Q2 canbe determined (FIG. 77) and the prescan graph adjusted to fit Q1 and Q2.Then the focus position P can be calculated as being in line with thelowest point on the parabola. It should be noted that while a linearcurve aspect of a parabola has been used for focus mapping in certainembodiments, the process described herein may be applied to other curvesusing a different underlying equation.

Enclosure Temperature Control

FIGS. 81A and 81B relate to controlling temperature within the upperenclosure 110 above the cassette. One skilled in the art will recognizethat the disclosed temperature control method can be used with othersystems and methods (such as other enclosed systems), such as otherenclosed systems, in addition to those disclosed herein. Referring toFIG. 3, the enclosure 110 is shown removed to expose a control board321. The control board 321 can include one or more temperature sensorsand a controller (e.g., processor, microcontroller, ASIC, etc.) forcontrolling a temperature control algorithm. The control board 321 canfurther include one or more of the components described in FIG. 101. Itis desirable to maintain a constant air temperature within the enclosure110 so that the biological samples are not adversely impacted bytemperature variations. However, due to space limitations, additionalheating and/or cooling components can prove to be difficult toincorporate into the design. Accordingly, the controller controlsexisting components that have a function unrelated to heating, in orderto heat the enclosure. For example, stepper motors that are not activelybeing used can have their idle currents increased so as to generateadditional heat to heat the enclosure.

A simple flow diagram is shown in FIG. 81B for dynamically controllingwaste heat. In process block 8110, the enclosure temperature controlflow diagram begins. Although described for stepper motors, the flowdiagram can be extended to other components that have a primary purposeunrelated to heating. In process block 8112, a temperature of theenclosure is read. For example, a temperature sensor, such as athermistor or thermocouple, can be used to provide temperatureinformation of a temperature within the enclosure. Such as temperaturesensor can be mounted on the control board 321. In process block 8114,the temperature reading can be used to compute aproportional-integral-derivative (PID) heater or fan output. Thecontroller on board 321 can use PID algorithms to compute an outputnumber. That output number can be a percentage of a maximum potentialoutput. PID algorithms are known in the art and any desired PIDalgorithm can be used. PID parameters may be provided—namely the P, Iand D terms, which act as multipliers in the PID algorithm to allowtuning of the algorithm to the application at hand. The PID algorithmcan be used to determine whether heating or cooling is needed, and, inaddition, what amount of heating or cooling is to be performed. Forexample, if the PID algorithm produces a control output that is anegative number then heating of the enclosure temperature is needed. Onthe other hand, if the PID algorithm outputs a positive number thencooling is needed. In addition, the output of the PID algorithm can havea range between a negative maximum and a positive maximum and the outputcan be a percentage of a maximum to further control heating or cooling.For example, the computed percentage can be used to adjust the speed ofthe fan with a corresponding percentage of the maximum fan speed. In asimple example, if the PID output is 70% of its maximum output, then thefan speed can be set at 70% of its maximum speed. Conversely, if heatingis required, then a number of components or percentage power increaseassociated with those components can be computed based on the percentageof the maximum PID output.

The stepper motors can have a minimum current requirement for holdtorque (when stepping motion is not occurring). However, additionalcurrent to the stepper motors is acceptable and will generate additionalwaste heat up to a maximum current as specified by the manufacturer ofthe stepper motor. Using the maximum and minimum values for the steppermotors as a 100% and 0%, a scale can be generated based on the PIDalgorithm output. If cooling is required, the stepper motors can havethe hold torque set to a minimum so that a minimum amount of waste heatis generated.

In process block 8116, a check is made of a state of a first steppermotor. The stepper motor can either be active (moving) or inactive (notmoving and maintaining its current position) for a predeterminedthreshold period of time (e.g., 5 seconds). When active, the steppermotor can be considered in an active mode. The controller can maintain atimer on each stepper motor and simply reset the timer when the motorbecomes active, which is also under the control of the controller. Inthis way, the controller can monitor the status of all of the steppermotors. Stepper motors that are not being used actively can be utilizedfor heat control, which is unrelated to movement. In decision block8118, a determination is made whether a motor is eligible for heatcontrol. If not, then a next motor is checked in process block 8120. Anexample of when a motor is not eligible is when the motor is active andperforming rotational motion of components in the system. In such astate, the stepper motor uses motion control parameters for its torque.If decision block 8118 is answered in the affirmative, then in processblock 8122, the motor idle torque is adjusted up or down according towhether the PID output indicated heating or cooling. For example, if thePID algorithm indicated that the temperature of the enclosure should beheated, then the controller can transmit a command to the stepper motorto increase its idle torque current to a desired value. In oneparticular implementation, an Application Programming Interface (API)request can be sent from the controller to the stepper motor with aparameter used to set the idle torque current. A check can be made on amaximum current of the stepper motor and a percentage of the maximumcurrent can be used based on the PID output. In the case the enclosureshould be cooled, the idle torque current can be set to the minimum,which is the minimum required to hold the current position. Thus, poweris decreased to the components, which includes setting currentsassociated with the components to minimum allowable currents. After thecurrent stepper motor has its idle torque current set, in decision block8124, a check is made whether all motors have had their idle torquecurrent set. If not, the process returns to process block 8120 to selecta next motor. Decision blocks 8118 and 8122 are then repeated. After allthe motors have had their idle torque current adjusted, then in processblock 8126, the enclosure fan 320 can have its speed adjusted. Forexample, if the PID output indicates that heating of the enclosure isneeded, then the fan 320 can be turned off. Alternatively, if cooling ofthe enclosure is needed, the fan can be activated and the holding torqueon each idle stepper motor is set to a minimum (the current needed tomaintain the motor position). The speed (RPM) of the fan is variable andcan be controlled based on the PID output. In process block 8128, theenclosure temperature routine ends. The routine can be rerun atpredetermined intervals so as to consistently control temperature withinthe enclosure.

A separate flowchart shows the process used when a stepper motor isactivated to perform its normal stepper functionality. In process block8140, the routine starts. In process block 8142, a determination is madeby the controller that motor movement is required. In process block8146, the controller can then set the motor as ineligible for enclosureheat control. Typically, the motor is only eligible when it becomesinactive for a predetermined period of time. In process block 8148,normal motor torque is restored to the motor. In process block 8150, theflow terminates. If the motor is later stopped, it can become eligibleagain for heat control after a predetermined period of time has passed.

Thus, components that have a function that is typically not related toheating (e.g., motors) can have their current altered so as to maximizeheat generation. Alternatively, their current can be lowered and fanspeed increased to lower temperature.

A graph shown in FIG. 81A shows the enclosure temperature as a functionof power consumption. In order to raise the overall temperature, thecontroller increases power to various components so that the temperatureof the enclosure increases. Increasing power can be unrelated toimproving functionality of the individual components, but rather tosimply increase heat generation. Alternatively, when the controllerattempts to cool the enclosure, power consumption can be reduced.

Controller

In various embodiments, a system can comprise a computer operablyconnected to the instrument and configured to control instrumentoperations and/or receive, store and process data received from theinstrument. A computer can be a special purpose computer or a generalpurpose computer configured to run custom software. In variousembodiments, a computer can include a display. A display configured toreceive user inputs may be used, such as a touchscreen display. Anexample controller is shown in FIG. 101.

Reagent Cartridge

In various embodiments, a system can comprise a reagent cartridge. Areagent cartridge 3300 in accordance with various embodiments isillustrated in FIGS. 26-34. The reagent cartridge can be configured tohold and/or store one, two, three, four, five, or more (or even all)reagents and consumables required for an ID/AST operation in apredefined reagent arrangement within the cartridge. For example,reagents can include sterile deionized water, antimicrobial agents,buffers such as GEF buffer and EKC buffer, growth media, FISH-relatedreagents such as nucleic acid probes, permeabilization agents,stringency wash solutions, cell stains or dyes, and the like. Reagentsmay be included as solutions or as dried or lyophilized reagents thatare reconstituted by the instrument during operation. With reference nowto FIGS. 26 and 27, reagents may be sealed in a plurality of reagentwells 3310 distributed around the reagent cartridge. The reagent wellsmay be sealed with a foil seal pierceable by the instrument pipettorfitted with a pipette tip, such as a pipette tip 3441. Consumables caninclude suitable pipette tips 3441 for use with the instrument pipettor,such as aerosol barrier filter tips. In various embodiments, thecartridge may also comprise subassemblies for performing various samplepreparation functions, such as one or more GEF apparatuses, groups ofreagent wells, or consumables racks (e.g., a pipette tip rack).Cartridge subassemblies may be operatively connected to or exposed toportions of the reagent stage, such as heating elements and electricalcontacts (e.g., GEF contacts).

The reagent cartridge 3300 may be constructed of a polymer or othersuitable material. The reagent cartridge may comprise polymer housing(or other suitable material) 3320. The housing may have a generallyrectangular shape (with rounded corners and curved ends) and may definea circular opening 3330 within the housing configured to accommodateindependent operation of the cassette stage with an attached cassetteand access to the cassette by other components of the instrument, suchas the pipettor, the illuminator, and the optics system. The housing3320 may be configured with a plurality of ports 3312 in the uppersurface of the housing, with the ports providing access by the pipettorto wells containing various reagents or cartridge subcomponents. Theupper surface of the housing may also comprise one or more teachingwells 3314. A teaching well 3314 can be an opening in the cartridgehousing, such as a circular opening, that the pipettor may use tospatially orient itself to the cartridge, such as in an initial pipettororientation step after user insertion of the cartridge and initiation ofan ID/AST process and/or in periodic pipettor orientation stepsthroughout instrument operation. In various embodiments, a teaching postor other structural feature that extends from the reagent cartridge maybe used as a teaching feature for pipettor orientation. The locations ofall cartridge ports or other openings may be known (i.e., addressed,such as using a coordinate system) relative to the location of theteaching well(s) or other teaching feature. Likewise, the contents ofthe cartridge at each cartridge port or opening may be associated withthe port or opening location by the system based on one of user inputregarding the reagent cartridge used for an ID/AST process orinformation acquired by the instrument from the cartridge itself, suchas a barcode or other identifier that may be sensed by the instrument.

The reagent cartridge housing may be configured to provide a frictionfit with alignment walls of the instrument reagent stage. For example,the width of the reagent cartridge may be configured to interface withthe alignment walls on either side of the cartridge. The reagentcartridge housing may further include on or more features such as acleat 3316 that interfaces with holding features coupled to thealignment walls of the reagent stage, further providing for secureinsertion and precise positioning of the reagent cartridge in thereagent stage, such as by providing a positive stop for reagentcartridge insertion into the reagent stage and/or a snap friction fit.

The reagent cartridge may include a pipette tip rack 3340. The pipettetip rack 3340 may be integral to the reagent cartridge housing, or thepipette tip rack may be a separate component that is assembled into thereagent cartridge housing by a friction fit or by any other suitableattachment mechanism.

The reagent cartridge housing may comprise two or more separate housingcomponents. For example, a housing may comprise a lower housingcomponent 3322 and an upper housing component 3324. The lower and upperhousing components may be attached to one another by a friction fit orby any other suitable attachment housing.

In various embodiments, the housing may define an interior space.Referring now to FIG. 31, the interior space may be configured to housea plurality of reagent cartridge components such as reagent racks 3801comprising reagent wells in various configurations, reagent tubs (e.g.,tub 3803), GEF wells 3805 configured to house GEF apparatus, and thelike. The lower housing component 3322 may include openings for accessand/or contact between the reagent stage and reagent cartridgecomponents, such as between reagent stage heating elements and reagentracks (e.g., opening 3807) Likewise, the lower housing component 3322may also include components such as electrical contacts to provide anelectrical interface between the reagent stage and one or more reagentcartridge components. For example, the reagent cartridge may includeelectrical contacts 3606 (FIG. 29) in a portion of the reagent cartridgehousing configured to engage GEF contacts 540 (FIG. 5) located on thereagent stage when the cartridge is inserted by a user. For example, atleast one electrical contact is accessible from an exterior of thereagent cartridge. In some examples, the at least one contact includes apair of spaced apart contacts positioned at one end of the reagentcartridge and configured for engagement by causing the reagent cartridgeto translate and engage other electrical contacts of the system.

At least some of the various wells and tubs included in the reagentcartridge may be sealed with a film, such as a film that may be piercedby a pipette tip mounted on the pipettor. The film may be located on theports of the reagent cartridge housing, or the film may be located onthe reagent wells and reagent tubs housed by the reagent cartridgehousing.

In various embodiments, certain reagents included in the reagentcartridge may be dried or lyophilized. For example, an antimicrobialagent may be dried into a film on the interior surface of a reagentwell. Dried or lyophilized reagents may be reconstituted by theinstrument by addition of water or buffers included in the reagentcartridge.

Reagents included in the reagent cartridge may include any of a varietyof reagents that may be used to perform microorganism ID and AST. Forexample, reagents can include water, various detergents or buffers,growth media, permeabilization agents, probes, dyes or stains, solvents,and the like.

The reagent cartridge may comprise a part of a reagent cartridge kit3400 (FIGS. 27-31 and 34). The reagent cartridge kit may be packaged ina sealed package, with each of the reagent cartridge kit componentsintended to be used for a single ID/AST operation. The reagent cartridgekit may further include a sample vial 3452 (for example, a sample vialinsertable in the reagent cartridge) and a cassette 3454. The cassettemay be housed or set in a disposable cassette holder 3455 configured tosit within a circular opening in the body of the reagent cartridge 3300configured to accommodate operation of the cassette and cassette stagewhen the reagent cartridge is inserted in the instrument. For example,the cassette holder may be shaped to receive the cassette, for example,having an outer dimension shaped for coupling the cassette holder overor in the opening in the reagent cartridge. The cassette and the pipettetip rack 3440 containing pipette tips 3441 may further be covered with aremovable seal 4257 (FIG. 34) within the sealed cartridge kit package.

In operation, an instrument user may obtain a reagent cartridge kit,remove the reagent cartridge from the sealed package, remove the samplevial from the cartridge to place a clinical or research specimen in thesample vial, remove the film seal cover from the cassette housing andpipette tip rack, remove the cassette and/or cassette housing from thecartridge, and place the cartridge in the instrument, placing thecartridge in the reagent stage. In accordance with various embodiments,different reagents may be included in a reagent cartridge and cartridgescomprising different sets of reagents may be offered as kits fordifferent research and diagnostic purposes. Different cartridge kits maybe offered for different clinical specimen types or differentdifferential diagnoses. For example, different cartridge kits may beoffered for ID/AST of blood cultures, bronchiolar lavage specimens,sputum, wound infections, urine samples, and the like. The reagentsincluded in the reagent cartridge may vary according to kit type, withdifferent FISH probes, sample preparation reagents, and antimicrobialagents included in different kits in accordance with the specimen typeand anticipated pathogens that may be present the specimen type. Forexample, various Gram-positive and Gram-negative bacteria and fungi(e.g., yeasts) can be identified using an ID/AST cartridge kit for apositive blood culture assay, including but not limited to:Staphylococcus aureus, Staphylococcus lugdunensis, coagulase-negativeStaphylococcus species (Staphylococcus epidermidis, Staphylococcushaemolyticus, Staphylococcus hominis, Staphylococcus capitis, notdifferentiated), Enterococcus faecalis, Enterococcus faecium(Enterococcus faecium and other Enterococcus spp., not differentiated,excluding Enterococcus faecalis), Streptococcus pneumoniae,Streptococcus pyogenes, Streptococcus agalactiae, Streptococcus spp.,(Streptococcus mitis, Streptococcus pyogenes, Streptococcusgallolyticus, Streptococcus agalactiae, Streptococcus pneumoniae, notdifferentiated), Pseudomonas aeruginosa, Acinetobacter baumannii,Klebsiella spp. (Klebsiella pneumoniae, Klebsiella oxytoca, notdifferentiated), Escherichia coli, Enterobacter spp. (Enterobactercloacae, Enterobacter aerogenes, not differentiated), Proteus spp.(Proteus mirabilis, Proteus vulgaris, not differentiated), Citrobacterspp. (Citrobacter freundii, Citrobacter koseri, not differentiated),Serratia marcescens, Candida albicans, and Candida glabrata.

Other specific bacteria that can be detected with the disclosed systemsand methods, include without limitation: Acinetobacter baumannii,Actinobacillus spp., Actinomycetes, Actinomyces spp. (such asActinomyces israelii and Actinomyces naeslundii), Aeromonas spp. (suchas Aeromonas hydrophila, Aeromonas veronii biovar sobria (Aeromonassobria), and Aeromonas caviae), Anaplasma phagocytophilum, Alcaligenesxylosoxidans, Actinobacillus actinomycetemcomitans, Bacillus spp. (suchas Bacillus anthracis, Bacillus cereus, Bacillus subtilis, Bacillusthuringiensis, and Bacillus stearothermophilus), Bacteroides spp. (suchas Bacteroides fragilis), Bartonella spp. (such as Bartonellabacilliformis and Bartonella henselae, Bifidobacterium spp., Bordetellaspp. (such as Bordetella pertussis, Bordetella parapertussis, andBordetella bronchiseptica), Borrelia spp. (such as Borrelia recurrentis,and Borrelia burgdorferi), Brucella sp. (such as Brucella abortus,Brucella canis, Brucella melintensis and Brucella suis), Burkholderiaspp. (such as Burkholderia pseudomallei and Burkholderia cepacia),Campylobacter spp. (such as Campylobacter jejuni, Campylobacter coli,Campylobacter lari and Campylobacter fetus), Capnocytophaga spp.,Cardiobacterium hominis, Chlamydia trachomatis, Chlamydophilapneumoniae, Chlamydophila psittaci, Citrobacter spp. Coxiella burnetii,Corynebacterium spp. (such as, Corynebacterium diphtheriae,Corynebacterium jeikeum and Corynebacterium), Clostridium spp. (such asClostridium perfringens, Clostridium difficile, Clostridium botulinumand Clostridium tetani), Eikenella corrodens, Enterobacter spp. (such asEnterobacter aerogenes, Enterobacter agglomerans, Enterobacter cloacaeand Escherichia coli, including opportunistic Escherichia coli, such asenterotoxigenic E. coli, enteroinvasive E. coli, enteropathogenic E.coli, enterohemorrhagic E. coli, enteroaggregative E. coli anduropathogenic E. coli) Enterococcus spp. (such as Enterococcus faecalisand Enterococcus faecium) Ehrlichia spp. (such as Ehrlichia chafeensiaand Ehrlichia canis), Erysipelothrix rhusiopathiae, Eubacterium spp.,Francisella tularensis, Fusobacterium nucleatum, Gardnerella vaginalis,Gemella morbillorum, Haemophilus spp. (such as Haemophilus influenzae,Haemophilus ducreyi, Haemophilus aegyptius, Haemophilus parainfluenzae,Haemophilus haemolyticus and Haemophilus parahaemolyticus, Helicobacterspp. (such as Helicobacter pylori, Helicobacter cinaedi and Helicobacterfennelliae), Kingella kingii, Klebsiella spp. (such as Klebsiellapneumoniae, Klebsiella granulomatis and Klebsiella oxytoca),Lactobacillus spp., Listeria monocytogenes, Leptospira interrogans,Legionella pneumophila, Leptospira interrogans, Peptostreptococcus spp.,Moraxella catarrhalis, Morganella spp., Mobiluncus spp., Micrococcusspp., Mycobacterium spp. (such as Mycobacterium leprae, Mycobacteriumtuberculosis, Mycobacterium intracellulare, Mycobacterium avium,Mycobacterium bovis, and Mycobacterium marinum), Mycoplasm spp. (such asMycoplasma pneumoniae, Mycoplasma hominis, and Mycoplasma genitalium),Nocardia spp. (such as Nocardia asteroides, Nocardia cyriacigeorgica andNocardia brasiliensis), Neisseria spp. (such as Neisseria gonorrhoeaeand Neisseria meningitidis), Pasteurella multocida, Plesiomonasshigelloides. Prevotella spp., Porphyromonas spp., Prevotellamelaninogenica, Proteus spp. (such as Proteus vulgaris and Proteusmirabilis), Providencia spp. (such as Providencia alcalifaciens,Providencia rettgeri and Providencia stuartii), Pseudomonas aeruginosa,Propionibacterium acnes, Rhodococcus equi, Rickettsia spp. (such asRickettsia rickettsii, Rickettsia akari and Rickettsia prowazekii,Orientia tsutsugamushi (formerly: Rickettsia tsutsugamushi) andRickettsia typhi), Rhodococcus spp., Serratia marcescens,Stenotrophomonas maltophilia, Salmonella spp. (such as Salmonellaenterica, Salmonella typhi, Salmonella paratyphi, Salmonellaenteritidis, Salmonella cholerasuis and Salmonella typhimurium),Serratia spp. (such as Serratia marcesans and Serratia liquifaciens),Shigella spp. (such as Shigella dysenteriae, Shigella flexneri, Shigellaboydii and Shigella sonnei), Staphylococcus spp. (such as Staphylococcusaureus, Staphylococcus epidermidis, Staphylococcus hemolyticus,Staphylococcus saprophyticus), Streptococcus spp. (such as Streptococcuspneumoniae (for example chloramphenicol-resistant serotype 4Streptococcus pneumoniae, spectinomycin-resistant serotype 6BStreptococcus pneumoniae, streptomycin-resistant serotype 9VStreptococcus pneumoniae, erythromycin-resistant serotype 14Streptococcus pneumoniae, optochin-resistant serotype 14 Streptococcuspneumoniae, rifampicin-resistant serotype 18C Streptococcus pneumoniae,tetracycline-resistant serotype 19F Streptococcus pneumoniae,penicillin-resistant serotype 19F Streptococcus pneumoniae, andtrimethoprim-resistant serotype 23F Streptococcus pneumoniae,chloramphenicol-resistant serotype 4 Streptococcus pneumoniae,spectinomycin-resistant serotype 6B Streptococcus pneumoniae,streptomycin-resistant serotype 9V Streptococcus pneumoniae,optochin-resistant serotype 14 Streptococcus pneumoniae,rifampicin-resistant serotype 18C Streptococcus pneumoniae,penicillin-resistant serotype 19F Streptococcus pneumoniae, ortrimethoprim-resistant serotype 23F Streptococcus pneumoniae),Streptococcus agalactiae, Streptococcus mutans, Streptococcus pyogenes,Group A streptococci, Streptococcus pyogenes, Group B streptococci,Streptococcus agalactiae, Group C streptococci, Streptococcus anginosus,Streptococcus equismilis, Group D streptococci, Streptococcus bovis,Group F streptococci, and Streptococcus anginosus Group G streptococci),Spirillum minus, Streptobacillus moniliformi, Treponema spp. (such asTreponema carateum, Treponema petenue, Treponema pallidum and Treponemaendemicum, Tropheryma whippelii, Ureaplasma urealyticum, Veillonellasp., Vibrio spp. (such as Vibrio cholerae, Vibrio parahemolyticus,Vibrio vulnificus, Vibrio parahaemolyticus, Vibrio vulnificus, Vibrioalginolyticus, Vibrio mimicus, Vibrio hollisae, Vibrio fluvialis, Vibriometchnikovii, Vibrio damsela and Vibrio furnisii), Yersinia spp. (suchas Yersinia enterocolitica, Yersinia pestis, and Yersiniapseudotuberculosis) and Xanthomonas maltophilia among others.

Other specific fungi that can be detected with the disclosed systems andmethods, include without limitation: Candida spp. (such as Candidaalbicans, Candida glabrata, Candida tropicalis, Candida parapsilosis,and Candida krusei), Aspergillus spp. (such as Aspergillus fumigatous,Aspergillus flavus, Aspergillus clavatus), Cryptococcous spp. (such asCryptococcus neoformans, Cryptococcus gattii, Cryptococcus laurentii,and Cryptococcus albidus), Fusarium spp. (such as Fusarium oxysporum,Fusarium solani, Fusarium verticillioides, and Fusarium proliferatum),Rhizopus oryzae, Penicillium marneffei, Coccidiodes immitis, andBlastomyces dermatitidis.

The ID/AST cartridge kit for a positive blood culture assay may beindicated as an aid in the diagnosis of bacteremia and fungemia. It mayalso be indicated for susceptibility testing of specific pathogenicbacteria commonly associated with or causing bacteremia. Resultsoptimally should be used in conjunction with other clinical andlaboratory findings.

An ID/AST cartridge kit (such as one for use with a blood culture assay)may include the following antimicrobial agents: amikacin, ampicillin,ampicillin-sulbactam, aztreonam, ceftazidime, ceftaroline, cefazolin,cefepime, ceftriaxone, ciprofloxacin, colistin, daptomycin, oxycycline,erythromycin, ertapenem, gentamicin, imipenem, linezolid, meropenem,minocycline, piperacillin-tazobactam, trimethoprim-sulfamethoxazole,tobramycin, vancomycin, or combinations of two or more thereof.Additional antimicrobial agents that may be used in the systems andmethods disclosed herein also include aminoglycosides (including but notlimited to kanamycin, neomycin, netilmicin, paromomycin, streptomycin,and spectinomycin), ansamycins (including but not limited to rifaximin),carbapenems (including but not limited to doripenem), cephalosporins(including but not limited to cefadroxil, cefalotin, cephalexin,cefaclor, cefprozil, fecluroxime, cefixime, cefdinir, cefditoren,cefotaxime, cefpodoxime, ceftibuten, and ceftobiprole), glycopeptides(including but not limited to teicoplanin, telavancin, dalbavancin, andoritavancin), lincosamides (including but not limited to clindamycin andlincomycin), macrolides (including but not limited to azithromycin,clarithromycin, dirithromycin, roxithromycin, telithromycin, andspiramycin), nitrofurans (including but not limited to furazolidone andnitrofurantoin), oxazolidinones (including but not limited to posizolid,radezolid, and torezolid), penicillins (including but not limited toamoxicillin, flucloxacillin, penicillin, amoxicillin/clavulanate, andticarcillin/clavulanate), polypeptides (including but not limited tobacitracin and polymyxin B), quinolones (including but not limited toenoxacin, gatifloxacin, gemifloxacin, levofloxacin, lomefloxacin,moxifloxacin, naldixic acid, norfloxacin, trovafloxacin, grepafloxacin,sparfloxacin, and temafloxacin), suflonamides (including but not limitedto mafenide, sulfacetamide, sulfadiazine, sulfadimethoxine,sulfamethizole, sulfamethoxazole, sulfasalazine, and sulfisoxazole),tetracyclines (including but not limited to demeclocycline, doxycycline,oxytetracycline, and tetracycline), and others (including but notlimited to clofazimine, ethambutol, isoniazid, rifampicin, arsphenamine,chloramphenicol, fosfomycin, metronidazole, tigecycline, andtrimethoprim), or any combination of two or more thereof. Furtherantimicrobial agents include amphotericin B, ketoconazole, fluconazole,itraconazole, posaconazole, voriconazole, anidulafungin, caspofungin,micafungin, flucytosine, or any combination of two or more thereof.

In one specific example, the reagent cartridge includes at least oneprobe (such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, or more) and at least one antimicrobial agent (such as1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,or more) in one or more wells of the reagent cartridge. In a particularnon-limiting embodiment, the reagent cartridge includes at least onewell including a Staphylococcus aureus probe, a well including acoagulase-negative staphylococci probe (for example, a probe hybridizingto S. epidermidis, S. haemolyticus, S. hominis, S. captis, notdifferentiated), a well including a Staphylococcus lugdunensis probe, awell including an Enterococcus faecalis probe, a well including anEnterococcus faecium probe, a well including a Streptococcus agalactiaeprobe, a well including a Streptococcus spp. probe (for example, a probehybridizing to S. mitis, S. gallolyticus, S. agalactiae, S. pneumoniae,not differentiated), a well including an Escherichia coli probe, a wellincluding a Klebsiella spp. probe (for example, a probe hybridizing toK. pneumoniae and K. oxytoca, not differentiated), a well including anEnterobacter spp. probe (for example, a probe hybridizing to E.aerogenes and E. cloacae, not differentiated), a well including aCitrobacter spp. probe (for example, a probe hybridizing to C freundiiand C. koseri, not differentiated), a well including a Proteus spp.probe (for example, a probe hybridzing to P. mirabilis and P. vulgaris,not differentiated), a well including a Serratia marcescens probe, awell including a Pseudomonas aeruginosa probe, a well including anAcinetobacter baumannii probe, a well including a Candida albicansprobe, a well including a Candida glabrata probe, a well includingamikacin, a well including ampicillin, a well includingampicillin/sulbactam, a well including aztreonam, a well includingcefazolin, a well including cefepime, a well including cefaroline, awell including cefazidime, a well including ciprofloxacin, a wellincluding colistin, a well including daptomycin, a well includingdoxycycline, a well including ertapenem, a well including erythromycin,a well including gentamicin, a well including imipenem, a well includinglinezolid, a well including meropenem, a well including minocycline, awell including macrolide-lincosamide-streptogramin B, a well includingcefoxitin, a well including piperacillin/tazobactam, a well includingstreptomycin, a well including tobramycin, a well includingtrimethoprim/sulfamethoxazole, and a well including vancomycin. In someexamples, each of the wells including a probe also includes a universalmicrobial or cell stain, such as acridine orange or propidium iodide.

Cassette

In accordance with various embodiments, a system can comprise acassette. The cassette may be configured to receive portions of thesample and other reagents in microfluidic sample channels. The cassetteand sample channels facilitate performing EKC, FISH ID, AST,pharmacodynamics testing, and other assays and imaging microorganismcells with the instrument illumination and optics systems. Cassettes inaccordance with various embodiments are illustrated in FIGS. 35-65 anddescribed in greater detail below.

In various embodiments and with reference to FIGS. 35-46, a cassette4500 may be a disk-shaped device comprising a glass support 4400, alaminate layer 4601, and a cassette top 4300 (see FIG. 46), alsoreferred to as a cassette component. The laminate layer 4601 maycomprise a polymer material with an adhesive layer or adhesive treatmenton both sides of the laminate layer polymer material. The glass support4400 may be adhered to the cassette top 4300 by the adhesive-treatedlaminate layer 4601. The three components may be pressed together toform the assembled cassette 4500. The assembled cassette 4500 canfeature a plurality of microfluidic sample channels 5702 (e.g., 48sample channels) (see FIGS. 37B and 57) arranged in a radialconfiguration in the cassette, with each sample channel having an inletport 4503 and an outlet port 4504 (FIG. 41), described in greater detailbelow. In some examples, the microfluidic channels are formed in thelaminate layer or in the laminate layer and in the cassette component.In various embodiments, the sample channels are oriented with the inletports 4503 toward the outer perimeter of the cassette and the outletports 4504 oriented toward the center of the cassette. In various otherembodiments, the inlet ports may be near the center of the cassette andthe outlet ports near the perimeter of the cassette. In someembodiments, the cassette includes a glass support adhered to a moldedpolymer disc and a plurality of microfluidic channels arranged in aradial configuration across the cassette.

As also shown in FIG. 41, the cassette 4500 may also include featuresfor user handling, such as a central cone or spindle 4505 and/or tabs4506 distributed around the perimeter of the cassette. Likewise, thecassette may include features for interfacing with a cassette stage,such as a plurality of locking spring arms 4507 around the perimeter ofthe cassette that provide a friction fit with a corresponding featurelocated on the cassette stage such that the cassette is stably andvibrationally coupled to the cassette stage. A common waste well 4515can be defined in a central area that is bounded by a circular wasteretaining wall 4519.

The cassette top 4300 can comprise a polymeric portion/region of thecassette that may be molded from an injection-moldable optically clearpolymer such as ZEONOR (e.g., ZEONORE 1060R) and ZEONEX. The bottomsurface of cassette top 4300 may be coated with an optically clearconductive layer such as ITO, either entirely or in the area of thesample channel, such as the bottom surface 4308 of the cassette top 4300forming the top of a sample channel flowcell (i.e., a flowcell-formingrelief area 4309 of a cassette top 4300) as well as the side walls of aflowcell-forming relief area in the bottom of the cassette top 4300. ITOmay be applied to the cassette top 4300 to provide a sheet resistance ofabout 5 to about 200 ohms/sq., or about 30 to about 170 ohms/sq., orabout 50 to about 120 ohms/sq., or about 70 to about 100 ohms/sq. Invarious embodiments, the sheet resistance may be less than about 100ohms/sq., or less than about 80 ohms/sq., or less than about 60ohms/sq., or less than about 40 ohms/sq., or less than about 20ohms/sq., or less than about 10 ohms/sq.

FIGS. 37A-37C show details of the cassette top 4300. FIG. 37B shows amask 4311 around a slot in the cassette top 4300. FIG. 41 shows asection of the cassette top in elevation illustrating one of the inletports 4503 in relation to its associated outlet port 4504. Selectsurfaces of the inlet port 4503 can be coated with ITO.

The glass support 4400 as shown in FIGS. 38-40 may comprise a flat,annular-shaped glass slide with an outer diameter and an interioropening 4410 defined by an interior diameter. The glass may haveproperties of high light transmittance and high chemical stability, suchas Schott B 270 i Ultra-White Glass or similar glass material. Theinterior opening 4410 may be configured to receive a central depressionof the plastic top portion of the cassette. ITO may be applied to asurface of the glass support to provide a sheet resistance of about 5 toabout 200 ohms/sq., or about 30 to about 170 ohms/sq., or about 50 toabout 120 ohms/sq., or about 70 to about 100 ohms/sq. In variousembodiments, the sheet resistance may be less than about 100 ohms/sq.,or less than about 80 ohms/sq., or less than about 60 ohms/sq., or lessthan about 40 ohms/sq., or less than about 20 ohms/sq., or less thanabout 10 ohms/sq. The glass support may be masked near the interioropening, such as is represented at region 4412 in FIG. 38, so that theITO coating does not extend to the edge of the interior opening 4410 ofthe glass support. The region can have boundaries of an inner diameter(ID) and an outer diameter (OD) as shown. Following ITO treatment, theglass support may be further coated with poly-L-lysine (“PLL”). As shownin FIG. 49, inner and outer edges of the glass support 4400 may be edgechipped or chamfered to the outside of the boundary 4414 and to theinside of the inner boundary 4416.

The cassette 4500 can include EKC slots 4520 and EKC holes 4522, whichare shown aligned in FIG. 41. In the illustrated embodiment, there arethree evenly spaced sets of the EKC slots 4520 and the EKC holes 4522.In addition, the cassette 4500 can include inner fiducial holes 4530 andouter fiducial holes 4532. In the illustrated implementation, there arethree evenly spaced sets of the inner fiducial holes 4530 and the outerfiducial holes 4532 that are interspersed between the three sets of theEKC slots and holes.

As best shown in FIG. 47A and the detail view of FIG. 47B, the areas ofeach EKC slot 4520 and each EKC hole 4522 can be selectively coated withsilver paint or other suitable substance to ensure conductivitythroughout the aligned slots and holes in the various components.

FIG. 48A and the detail view of FIG. 48B show the inner fiducial holes4530 and the outer fiducial holes 4532. Referring to FIG. 48B, afiducial mark 4534 on the glass support 4400 is positioned to becentered within a circular hole 4536 in the laminate layer 4601.Similarly, a fiducial mark 4538 on the glass support 4400 is positionedto be centered within a circular hole 4540 in the laminate layer 4601.

FIG. 53A shows a portion of a cassette without an ejector pad E (FIG.52A), in an alternate embodiment. FIG. 52B shows a portion of a cassetteand FIG. 53B shows the same portion in which pockets P have been formedin an alternate embodiment. FIG. 52C shows a hole in a portion of thecassette and FIG. 53C shows a chamfer C added to the hole H in analternate embodiment.

FIG. 65 is a plan view an alternate design of the cassette 4500, inwhich the inner slot 4520 is painted with silver paint after beingmasked when the glass support 4400 is coated with ITO, the fiducialmarks are aligned within the respective holes 4540 and 4536, and abarcode 4550 is provided.

FIGS. 54 and 55 are perspective views of alternative laminate layers. Insome examples, the alternative laminate layer 4601′ has a thickness ofapproximately 0.0035 inch, with a construction comprising 0.001 inch PSA(clear), 0.0015 inch PET (blue) and 0.001 inch PSA (clear). In someexamples, the alternative laminate layer 4601″ has a thickness ofapproximately 0.0135 inch, with a construction comprising 0.00175 inchPSA (clear or blue), 0.010 inch PET (clear) and 0.00175 inch PSA (clearor blue).

A schematic cross section of a cassette 4700 with a thinner laminatelayer is shown in FIG. 59. As shown, the gap between the glass supportand the cassette top is smaller, so fluid tends to be trapped in the gapduring processing. Because the gap is smaller, the EKC electrical fieldsare about four times greater than in implementations with larger gaps.

By contrast, FIG. 60 is a schematic cross section of a cassette 4702with a thicker laminate layer. As shown, the gap between the glasssupport and the cassette top is larger. The thicker laminate layer canbe cut more cleanly. In addition, the thicker PSA layer component mayprovide better sealing.

FIGS. 61 and 63 show top plan and bottom plan views, respectively, of acassette 4704 in which the channels are formed in both the cassette topand in the laminate layer. FIGS. 62 and 64 show top plan and bottom planviews, respectively, of a cassette 4706 in which the channels are formedonly in the laminate layer.

As best shown in FIG. 57, each individual sample channel 5702 comprisesone of the inlet ports 4503 in fluid communication with a flowcell 5711,with the flowcell further in fluid communication with the associated oneof the outlet ports 4504. The inlet port 4503 is configured to receive apipette tip attached to the pipettor assembly and to interface with thepipette tip in a manner that creates a seal suitable to substantiallyprevent fluid expelled from a pipette tip from filling the inlet portabove the tip end (i.e., fluid expelled from the tip is preferentiallydirected into the inlet port channel and the flowcell due to the sealcreated by the interface between the pipette tip and the inlet port).The inlet port 4503 may have a conical shape with an inside diameter atthe top of the inlet port that is greater than the outside diameter ofthe pipette tip. The inside diameter of the inlet port may decreasetoward the bottom of the inlet port, tapering toward an inlet portchannel 5712 that is smaller than the pipette tip to facilitatereceiving the pipette tip lowered into the inlet well by the pipettorand guiding the pipette tip to a position substantially coaxial with thevertically oriented inlet port channel. The inlet port structure may beconfigured so that the top of the inlet port and/or inlet port channelis located at a raised inlet port height in relation to the level of theflowcell in a manner configured to create a positive pressure head(i.e., a hydraulic head) with respect to the flowcell to further producegravity-driven fluid flow from the inlet port into the flowcell (i.e.,the sample channel design and configuration of the inlet port, thesample channel flowcell, and the outlet port are suitable to facilitatepipettor-driven fluid flow through the sample channel in response topositive pressure at the inlet port by the pipettor, as well as gravityand surface tension driven flow through the sample channel from theinlet port to the outlet port when each port is at ambient pressure andthe channel is fluid-filed with the fluid level reaching the tops of theinlet port channel and the outlet port channel (i.e., the outlet portnozzle)). In various embodiments, the configuration of the inlet port4503, the flowcell 5711 and the outlet port 4504 may be suitable toprevent fluid backflow when the sample channel is fluid-filled and thefluid level reaches the top of the inlet port channel and the outletport nozzle 4512, respectively (i.e., the sample channel is continuouslyfluid filled from the top of the inlet port channel to the top of theoutlet port channel (i.e., the outlet port nozzle)) with the inlet portand the outlet port at ambient pressure.

The flowcell 5711 can comprise a horizontally oriented chamberconfigured to facilitate distribution of sample objects across a regionof the cassette defined by the flowcell boundaries for EKC and imaging.In various embodiments, a flowcell may have a height of about 300 μm toabout 350 μm, a width of about 2.2 mm (in an imaging region), and alength of about 22.5 mm. A flowcell may comprise a flowcell imagingregion 5713 and a flowcell outlet region 5714. The flowcell imagingregion 5713 may be about 16.5 mm in length. The flowcell outlet region5714 may be about 6 mm in length and decrease or taper in width from thewidth of about 2.2 mm in the imaging region to a narrower width at theinterior end (i.e., located radially inward) of the flowcell locatednear the outlet port. FIG. 58 is an alternative view/perspective of FIG.57.

FIG. 56 is a drawing of a portion of a cassette showing a configurationhaving a misaligned channel, which may be disadvantageous, but still maybe functional under many conditions. The flowcell 5711 is an alternativedesign that avoids this challenge because there is no channelmisalignment, there is no fluid flow over the laminate layer and theexit transition is smoother. There is also increased tolerance forslight deviations in the imaging location. Further, the EKC gap isuniform in this embodiment, and no ITO is required in the sides of thechannel. The estimated change in the volume of the channel is on theorder of +1 microliter from the embodiment configured in FIG. 56.

In accordance with various embodiments, the configuration of theflowcell outlet region may reduce or substantially eliminate diffusionof waste products or contaminants from the outlet channel. While notwishing to be bound by theory, the time t required for a molecule totravel a distance L may be represented by the equation:t=L ²/(10D),where D is a diffusion coefficient that depends on the size and shape ofthe molecule in question as well as its interaction with the solvent andthe viscosity of the solvent. A value of D of 1E-3 mm²/s may be used torepresent a typical small molecule that may be present in a fluid usedin a cassette. Using this value of D in the equation above, the timerequired for a molecule to diffuse 1 mm is 1000 s. Thus, the timerequired for a molecule with the same diffusion coefficient to travel 6mm would be 3.6E4 s, or 10 hr. In various embodiments, reagents may beexchanged through the flowcell in a manner calculated to move wastereagents from the flowcell into the outlet channel, and the separationof the imaging region of the flowcell from the outlet port channel bythe flowcell outlet region may prevent solutes in the outlet channelfrom diffusing into the imaging region of the flowcell in a timeframe inwhich ID, AST, or other assays are performed.

An outlet port of a sample channel may be configured to permit excessfluid to exit the sample channel upon displacement by fluid expelledinto the inlet port by the pipettor. A cassette may be configured with acommon waste well 4515 that receives excess fluid exiting from the eachof the plurality of sample channel outlet ports 4504.

In various embodiments, an outlet port 4504 may be configured to preventfluid backflow from the outlet port or a waste well back to the samplechannel. In addition, a cassette and/or each sample channel outlet portmay be configured to encourage fluid flow into the common waste well4515. Pressure head differentials between an outlet port and an inletport (i.e., negative pressure head from the inlet port to the outletport) can produce backflow effects from the outlet port toward thesample channel. Likewise, a variety of phenomena can produce fluid flowagainst gravity that can result in backflow or contamination from acommon waste well, including various surface tension-induced effectssuch as capillary action, wicking, and Marangoni flows. Although the useof separate waste wells for each sample channel can alleviate concernregarding sample channel cross-contamination due to backflow, separatewaste wells for each channel requires increased cassette size orincreased fluid handling. Additionally, backflow from an individualsample channel waste well must still be prevented.

Various parameters of an outlet port may be configured to providedesired outlet port performance with respect to waste fluid. Theseparameters include (but are not limited to): the length, the diameter,the surface treatment of an outlet port channel; the orientation of theoutlet port channel end (i.e., the outlet port “nozzle”); theconfiguration and surface treatment of the surface surrounding theoutlet port nozzle; the height of the outlet port nozzle above commonwaste well; the distance of the outlet port channel structure (i.e., thestructure housing the outlet port channel) from adjacent outlet portchannel structures; and the like. These features may be configured toproduce capillary action within the outlet port channel and otherhydraulic effects with respect to droplet formation and droplet behaviorat the outlet port nozzle for the various fluids and fluid mixtures usedin the sample channel over the course of instrument operation to performID and AST. These parameters may be configured to reduce the occurrenceof backflow from the outlet port channel toward the sample channel andto prevent fluid flow from the common waste well into the outlet port.

In various embodiments, an outlet port may comprise a cylindricalchannel located above the end of the flowcell and oriented verticallyupward. The outlet port channel may have a small diameter suitable toprovide surface effects that create a level of capillary action toextend the height of the outlet port channel sufficiently above thecommon waste level to create a separation barrier while maintaining anegative pressure head relative to the inlet port filled to the top ofthe inlet port channel (at ambient pressure; i.e., without a positivepressure from the pipettor). The outlet port channel may be located inan outlet port structure 4516. The outlet port structure 4516 may beconfigured to extend vertically upward from the top surface 4517 of thecassette. The outlet port structure may define the outlet port channelas a substantially vertically oriented cylindrical channel within theoutlet port structure. The outlet port structure may comprise an arcuatefin or rib 4518 extending radially inward toward the central commonwaste well 4515. The plane of the outlet port nozzle may be orientedorthogonally to the outlet port channel axis. The area of the tops ofthe outlet port structures surrounding the outlet port nozzles and thedistance between the tops of adjacent outlet port structures may beconfigured to prevent waste fluid droplets that form at the outlet portnozzles from contacting the tops and/or nozzles of adjacent outlet portstructures. In various embodiments, the outlet port structure may beconfigured with a geometry having a ratio of height to distance betweenoutlet port structures that exceeds a critical value such that fluidsurface forces are insufficient to overcome fluid body forces, therebypreventing fluid backflow by wicking and/or capillary action. Stateddifferently, the height of each outlet port structure and the distancebetween adjacent outlet port structures may be sufficient to preventcapillary action of fluid in the common waste well from overcominggravity and reaching the tops of the outlet port structures. In variousembodiments, the arcuate conformation of the outlet port structure finmay wick a waste fluid droplet downward toward the common waste well.The configuration of the outlet port structures may provide forcapillary action between adjacent outlet port structures in a directionaway from the outlet port nozzles, drawing waste fluid downward and/orradially inward toward the center of the cassette and the common wastewell.

In various embodiments, the configuration of the cassette and the outletports does not require individual outlet ports or waste wells to beemptied by a pipettor. The common waste well can be emptied periodicallyif necessary when the pipettor is not required for other operations.

In various embodiments, the outlet port does not require a mineral oiloverlay or similar treatment to reduce or prevent evaporation in thesample channel. Fluid evaporation from the outlet port may reduce apressure head in the outlet port, producing fluid flow through thesample channel from the inlet port, reducing a fluid level in the inletport channel. In various embodiments, both the inlet port channel andthe outlet port channel may be configured such that evaporation of fluidfrom the open surfaces of each over the course of an assay such as ID orAST is less than a critical value such that each of the inlet portchannel and the outlet port channel store excess fluid volume and fluidvolume depletion by evaporation does not deplete fluid from the flowcelland/or otherwise influence the solution properties of the fluidcontained in the flowcell. In various other embodiments, the inlet portand/or the outlet port may be covered with mineral oil or any otherreagent suitable to overlay an aqueous fluid and reduce evaporation ofthe fluid.

In various embodiments, an outlet port may be configured to provide areduced pressure head relative to a pressure head of the inlet port,thereby reducing or substantially preventing backflow into the samplechannel. An outlet port may be configured to provide a reduced pressurehead by maintaining a small outlet port channel volume, such as byconfiguring the outlet port channel with a low outlet port channelheight. In various embodiments, an outlet port channel may be configuredwith a reduced channel diameter, such that a reduced outlet port channelvolume is provided per unit of outlet port channel height, and furtherproviding for an increased surface tension force (i.e., capillaryaction) on fluid in the outlet port channel that counteracts thegravitational force contributing to the pressure head. In this manner,the outlet port channel may be configured to maintain a low pressurehead while increasing the head height to prevent backflow from the wastewell into the outlet port. In accordance with various embodiments,configuring the outlet port to provide a low outlet port pressure headmay permit the sample channel inlet port to be configured with a lowhead height while providing a positive pressure head relative to theoutlet port, thereby reducing the height of the inlet port structurerequired to provide the positive pressure head at the inlet portrelative to the outlet port.

In various embodiments, the use of sample channels comprising an outletport channel diameter that is smaller than the inlet port and/or inletport channel permits an increased number of sample channels per unitsurface area as compared to a cassette comprising sample channels of thesame overall configuration but having an outlet port channel of the samediameter as the inlet port channel.

In various embodiments, a vertical outlet port channel configuration iscompatible with manufacturing in a single molding stage withoutadditional side-actions or pick outs.

Method of Operation

Overview

An instrument and system in accordance with various embodiments may beused to perform microorganism identification, quantitation, andantimicrobial susceptibility testing in an automated fashion. Systemarchitectures and process flows for a system and method of operation inaccordance with various embodiments are illustrated in FIGS. 69-75. Anoverview of the disclosed ID/AST methods is provided, with additionaldetails for various aspects of the methods provided in the followingsections.

In some embodiments, a patient sample (or portion thereof) is loaded ina sample vial and loaded into the instrument. Patients can include humanand veterinary subjects, such as cats, dogs, cows, pigs, horses, sheep,chickens and other birds, fish, and the like. In some examples, apatient is one who is known to have or is suspected of having aninfection (such as a bacterial or fungal infection). In one example, thepatient is septic. Patient samples include but are not limited to blood(e.g., whole blood, plasma, or serum), respiratory samples (such asbronchoalveolar lavage, oropharyngeal swab, nasopharyngeal swab, orsputum), saliva, urine, rectal swab, vaginal swab, tissue samples, orother biological specimens. Samples can be concentrated, diluted, and/orseparated before analysis with the disclosed method and systems.Portions of the sample are introduced into a plurality of microfluidicchannels in the cassette. One or more detectably labeled nucleic acidprobes (e.g., include a detectable label, such as a fluorophore) areintroduced into each of the microfluidic channels. In particularexamples, the detectably labeled probes include species-specific (alsoreferred to as target-specific) probes and/or universal probes. Eachspecies-specific probe specifically hybridizes to nucleic acids from aspecific target organism (e.g., E. coli) or group of target organisms(e.g., Klebsiella spp., such as K. pneumoniae, K. oxytoca, notdifferentiated). In some examples, the species-specific probes eachspecifically hybridize to rRNA of a target organism or group oforganisms. Each universal probe hybridizes to nucleic acids from a groupof class of organisms, such as all bacteria and/or fungi. In onespecific example, one species-specific probe is introduced into eachmicrofluidic channel (e.g., a different species-specific probe in eachchannel) and the universal probe is introduced into all of the channels.The detectably labeled probes are incubated with the sample underconditions sufficient for the probes to hybridize to microorganismspresent in the sample.

Images of one or more (such as 1-100, for example, 2-25, 10-40, 30-80,or 50-100) fields of view of one or more microorganisms are captured.Multiple images of the same field of view may be captured, for exampleunder one or more different imaging modalities. In some examples, eachfield of view is imaged under conditions to detect each of the labeledprobes (such as the species-specific probe and the universal probe) andoptionally under dark field conditions. The images are subjected tomorphological or other analysis (such as morphokinetic analysis) toidentify characteristics of the imaged microorganisms, including one ormore of signal intensity of the one or more detectably labeled probes,noise, cross-talk, and microorganism morphology. The information fromthe morphological analysis is input to a probability expectation modelof distribution to identify one or more microorganisms present in thepatient sample. In some examples, the microorganism is identified basedon a combination of posterior probabilities from one or more of theplurality of microfluidic channels. In some examples, the signal patternof the detected microorganism(s) in the sample are compared or matchedwith a posterior probability density function (PDF) of a labeled targetprobe using an empirical threshold value. In additional examples, theposterior distributions are passed through Kernel Density Estimator andintegrated to provide a resulting likelihood or probability of an event(e.g., presence or absence of a specific microorganism) in amicrofluidic channel. In some examples, the sample includes one microbe(such as one “target” microbe), while in other examples, the sampleincludes two or more microbes (for example, 2, 3, 4, or more), which isreferred to herein as a “polymicrobial” sample.

In some embodiments, the patient sample is subjected to one or morepre-processing steps prior to contacting the sample with the one or moredetectably labeled probes. These pre-processing steps include GEF (forexample, to remove or reduce lysed cells and debris in the sample)and/or EKC (for example to localize microorganisms to a surface foranalysis).

In additional embodiments, following microorganism identification, thepatient sample (or a subsample thereof) is subjected to AST analysis.Based on the identity of the microorganism(s) in the sample, a portionof the sample is grown in the presence or absence of one or moreantimicrobials and the growth of the identified microorganisms ismonitored over time (for example, at least 30 minutes, at least 1 hour,at least 2 hours, at least 3 hours, at least 4 hours, at least 5 hours,or at least 6 hours, such as about 1-6 hours). In some examples, thegrowth of the microorganisms is monitored by capturing images of themicroorganisms at repeated time intervals (such as every 10 minutes for4.5 hours) and qualitatively or quantitatively measuring the growth (oramount of growth), lack of growth, or lysis of the microorganisms. Basedon the behavior of the microorganisms over time in the presence of theone or more antimicrobials (for example, compared to a control that isnot exposed to the antimicrobial(s)), a determination of susceptibility(or indeterminate susceptibility) or resistance of the identifiedmicroorganisms to each antimicrobial is made. In some examples, themethods also include determining a MIC of the identified microorganismsto one or more antimicrobials.

In some embodiments, prior to AST analysis, the concentration of themicroorganism(s) in the patient sample is determined, in order tocalculate an appropriate dilution of the sample to utilize in AST. Insome examples, the concentration is determined using the dynamicdilution methods described herein. In additional embodiments, the sampleor portion thereof is subjected to one or more pre-processing stepsprior to AST, for example GEF and/or EKC.

User Input

An instrument user may perform various input functions to prepare aninstrument for operation and to prepare the system components and asample for processing. For example, a user may obtain a sample, such asa biological or clinical specimen. The user may also obtain a packagecomprising a reagent cartridge kit. The reagent cartridge kit containedin the package may further comprise a sample vial and a cassette. Theuser may transfer all or a portion of a biological or clinical specimento the sample vial and load the reagent cartridge and sample vial in theinstrument. The user may also insert the cassette into the instrument.The user may enter order information into the controller via a userinterface. In various embodiments, the instrument may comprise one ormore devices for scanning a barcode or otherwise recognizing informationencoded on a cartridge, cassette, and/or sample vial. The instrument mayrecord various information and/or perform instrument functions based oninformation encoded on the cartridge, cassette, and/or sample vial andrecognized by the instrument itself. After all sample preparation,sample loading, and order entry steps are completed, the user may pressa “Run” button located on the instrument to initiation the ID/ASTprocess. In accordance with various embodiments, the user input stepsthat must be completed by a user prior to initiation of an instrumentrun may be completed by a user in about 15 minutes or less.

Automated Sample Preparation

In various embodiments, following sample loading, an automated samplepreparation step is performed. Sample preparation may be performed usinggel electrofiltration (“GEF”), such as in the manner described in U.S.Patent Publication No. 2014/0038171A1, which is hereby incorporated byreference in its entirety. An aliquot of the sample may be removed fromthe sample vial by the instrument pipettor and transferred to a GEFdevice. A GEF step is performed to separate sample debris frommicroorganism cells that may be present in the sample. Following the GEFstep, the electric field is briefly reversed to displace microorganismcells from the surface of the gel matrix and the prepared samplecomprising microorganism cells present in the sample can be removed fromthe GEF device. In accordance with various embodiments, the instrumentand system may be configured to perform two separate GEF steps, with afirst GEF step performed to prepare a portion of the sample for FISH ID,and a second GEF step performed to prepare a portion of the sample forAST. Any number of GEF sample preparation steps may be performed toprepare one or more portions of a sample for various assays that may beperformed by an instrument in accordance with various embodiments. Thesample may be a polymicrobial patient sample in which multiple strains,species or types of microorganisms are present (such as at least 2, atleast 3, at least 4, or at least 5 different strains, species or typesof microorganisms). The sample may be a direct-from-patient sample.

Cell Immobilization

Following sample preparation by GEF, recovered sample is aliquoted bythe pipettor into one or more sample channels of a cassette. In variousembodiments, EKC is performed to capture cells on a capture surface ofeach sample channel flowcell. In other embodiments, cells areimmobilized by entombing them in three-dimensional space in a mediumthat can be phase changed after contacting the cells (e.g., agar). Theentombing can occur with or without first performing the capture step.In some embodiments, the entombing creates a microenvironment around theimmobilized microorganism, the characteristics of which are notinfluenced by neighboring microorganisms during the identificationand/or susceptibility testing periods. In some examples, the methodincludes retaining the microorganism on a detection surface of thesample channel, thereby producing a retained microorganism, andsubsequently introducing a gel medium (such as one containing agar) intothe sample channel, wherein the gel medium is in contact with theretained microorganism following introduction into the sample channel;immobilizing the retained microorganism in the sample channel at thesame location where the microorganism is retained, to produce animmobilized microorganism, wherein offspring of the immobilizedmicroorganism remain over time at a location with the immobilizedmicroorganism; and incubating the immobilized microorganism for a periodof time to allow for growth of the microorganism.

FISH Identification

Microorganism identification may be performed by hybridization of one ormore (such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, or more) species-specific probes and/or one or moreuniversal microbial probes (such as a universal bacterial and/oruniversal fungal probe) in a FISH assay. For example, in specificembodiments, seventeen different species-specific probes, each designedto hybridize to the ribosomal RNA of a specific microorganism species(e.g., the “target”), and a “universal” FISH probe, are prepared by theinstrument using reagents contained in the reagent cartridge and themixtures introduced into seventeen sample channels. Cellpermeabilization and washing steps may be performed by the instrumentusing reagents contained in the reagent cartridge, followed by ahybridization step in which the species-specific probes attach to atarget microbial species if cells of the target microbial species arepresent in the sample and captured in the sample channel. The universalprobe hybridizes to any microorganism nucleic acid molecules present. Invarious embodiments, probe migration and hybridization to microorganismnucleic acids may be accelerated by application of an electrical fieldin the flowcell by the instrument. Unattached (e.g., non-hybridized)probes may be rinsed out of the channel by the instrument followinghybridization.

Each FISH probe has an attached fluorophore that emits light at a peakemission wavelength when illuminated with an appropriate excitationwavelength. For example, the species specific probes may be labeled witha first fluorophore, such as ATTO-532, that provides for emissiondetection at a first emission wavelength, such as 553 nm, whenilluminated by the illuminator using a first laser, such as a greenlaser providing a first excitation wavelength, such as 520 nm. In someembodiments, each of the species-specific probes is labeled with thesame fluorophore, while in other embodiments, one or more of thespecies-specific probes is labeled with a different fluorophore than atleast one of the species-specific probes.

The “universal” FISH probe is configured to hybridize to a plurality ofmicroorganism species, and in some examples is used in conjunction witha second laser, such as a red laser. Use of a universal probe may serveto facilitate identification of polymicrobial specimens comprising morethan one bacterial or fungal species and observing the presence ofbacteria not marked by any of the seventeen species-specific probes. Invarious embodiments, the fluorophore used for a universal probe cancomprise a second fluorophore (e.g., a different fluorophore than thatused to label one or more of the species-specific probes), such asATTO-647, that provides for emission detection at a second emissionwavelength, such as 669 nm, when illuminated by the illuminator using ared laser providing a second excitation wavelength, such as 637 nm.

Additional fluorophores for labeling the species-specific and oruniversal probes can be utilized. Exemplary fluorophores include but arenot limited to Alexa Fluor® fluorophores, coumarin, fluorescein (FITC),rhodamine, rhodamine Green, rhodamine Red, tetramethylrhodamine (TRITC),Cy®3, Cy®7, and Cy®5 fluorophores, Pacific Green™, Oregon Green™,Pacific Blue™, Pacific Orange™, and Texas Red® fluorophores,PlatinumBright™ fluorophores (Leica), 6-FAM, TAMRA, JOE(6-carboxy-4′,5′-dichloro-2′,7′-dimethoxyfluorescein), VIC,tetrachlorofluorescein (TET), hexachlorofluorescein (HEX), ROX(carboxy-X-rhodamine), IRDyes® fluorophores (Li-Cor), and ATTO™fluorophores. One of ordinary skill in the art can select suitablefluorophores and combinations of fluorophores for labeling thespecies-specific and/or universal probes utilized in the systems andmethods described herein.

In various embodiments, the system may exclude sample debris fromanalysis by considering only image objects colocalized using bothdark-field imaging and fluorescence imaging during image processing bythe controller. An example of FISH ID image data acquired using the AD-1instrument is illustrated in FIG. 66.

In various embodiments, a universal control stain (such as a DNAintercalating dye) may be combined with a prepared sample and introducedinto a sample channel. Exemplary universal control stains include butare not limited to acridine orange, propidium iodide, and4′6-diamidino-2-phenylindole (DAPI). Image data from all flowcells maybe used to determine the total number of organisms present in a sampleand permit evaluation of the sample for the presence of non-targetorganisms and polymicrobial samples.

In accordance with various embodiments, the instrument can report FISHID results approximately one hour after sample loading. The ID resultsmay be used by the system to determine the selection of appropriateantibiotics or antifungals for performing AST from the antimicrobialreagent panel included in the reagent cartridge.

Direct-from-patient samples (such as blood, urine, respiratory samples)are generally complex milieus, containing live cells, dead cells anddebris that interfere with testing procedures. Typical methods for theidentification of microorganisms in patient samples often results inambiguous quantitation results. Potential errors include organismmisclassification, wrong identification and improper quantification ofviable organisms. The problem has been addressed in the past bynon-quantitative operator judgment. The instrument system employs adynamic dilution procedure with automated interpretation of data frommultiplexed channels and probes to improve accuracy and reduce errors inmicrobial quantification. Once the microorganisms in a sample areidentified, aliquots of the sample may be subjected to antimicrobialsusceptibility testing to determine the best course of therapy to treatthe particular strain of microorganism (or strains of multiplemicroorganisms) infecting an individual.

Antimicrobial Susceptibility Testing (AST)

In various embodiments, the instrument may combine a sample prepared byGEF (or a subsample thereof) with growth media and subject the sample toa pre-growth step during the approximately 1-hour FISH ID assay. Thepre-growth step performed by the instrument may normalize microorganismgrowth rates prior to AST. The instrument may determine organismconcentration in the pre-grown sample by performing (and/or repeating) acell quantitation process using a universal nucleic acid-binding stain.Based on the organism concentration of a sample following the pre-growthstep, the instrument may dilute the sample to provide an appropriatecell concentration for AST. Following automated sample preparation andEKC, the instrument may add growth media (e.g., MHA) containing singleconcentrations of each test antimicrobial (such as antibiotics preparedby the instrument) to separate sample channels. The instrument mayprovide temperature control of the enclosure, the cassette, and reagentsin the reagent cartridge. In various embodiments, different temperaturesmay be provided by the instrument over time and/or for specificcomponents of the instrument. For example, the instrument may provideseparate temperature control and temperature profiles (e.g., changes intemperature over the time course of an assay and/or instrumentoperation) for the subassemblies of the cassette that contain the MHAand the antibiotic reagents. Following distribution of growth media tothe sample channels, the instrument may periodically acquirephotomicrographs of microorganism cells in each sample channel flowcellfor an AST assay period, for example, about every 5-30 minutes (such asabout every 5 minutes, 10 minutes, 15 minutes, 20 minutes, 25 minutes,or 30 minutes) for about 1 to 8 hours, such as up to about 1.5 hours, 2hours, 3 hours, 4 hours, 4.5 hours, 5 hours, 6 hours, 7 hours, or 8hours, creating a time-lapse record of microorganism growth. Examples ofphotomicrograph images for susceptible and resistant microorganisms attime=0 hr, 1.5 hr, 3 hr, and 4.5 hr are shown in FIG. 67.

During the AST process, various microorganism clone features aremeasured, such as division rates of specific species and the lightintensity of a growing clone, and used for analysis. For example, aquantitative measurement of an individual clone growth rate over timecan be used to determine antimicrobial efficacy. Onboard softwarealgorithms determine quantitative identification of the targetorganism(s), derive minimum inhibitory concentration (MIC) values, andapply appropriate expert rules for proper interpretation and reportingof categorical interpretations such as “S,” “I,” or “R” (susceptible,intermediate, or resistant) or “S” or “NS” (susceptible or notsusceptible). In accordance with various embodiments, AST can beperformed in approximately 5 hours. Examples of microorganism growthcurves demonstrating S, I, and R growth interpretations are illustratedin FIG. 68.

In some embodiments, the system reports susceptibility, intermediate, orresistance to one or more antimicrobials (e.g., susceptible to one ormore of the antimicrobials in Table 5 or other antimicrobial providedherein) and/or an MIC for one or more antimicrobials. In otherembodiments, the following resistance phenotypes may be reported by thesystem in response to AST data analysis: Methicillin-resistantStaphylococcus aureus (MRSA), methicillin-resistant staphylococci (MRS),vancomycin-resistant S. aureus (VRSA), vancomycin-resistant Enterococcusspecies (VRE), high-level aminoglycoside resistance (HLAR) andmacrolide-lincosamide-streptogramin B resistance (MLSb).

Dynamic Dilution

In general, susceptibility testing requires that standard concentrationsof viable inoculates be combined with set concentrations ofantimicrobial agents. Accordingly, in order to perform an antimicrobialsusceptibility test on an unknown bacterial sample, the startingconcentration of the sample inoculum must be determined. Commonly, theinoculum concentration is determined by plating—which takes one to twodays to complete—or through use of a McFarland turbidity assay. Althoughfaster than traditional plating assays, turbidometric measure ofbacterial cell concentration in a sample is not highly accurate, as thismethod only provides an approximation of colony forming units. Thisapproximation is calculated from the amount of scatter achieved whenlight passes through a suspension of microorganisms, which serves as anindicator of the biomass of cells present in the suspension.

Because turbidity assays rely on light scattering through a bacterialculture, these assays neither directly count the number of bacterialcells present, nor do they directly measure colony forming units as isaccomplished with plating. According to the United States Pharmacopeia,the estimated measure of cells obtained via a turbidity assay should beconfirmed by a plate count as a control after the test is underway. Theconfirmation value is then utilized to calibrate the size of theinoculum used in the subsequent susceptibility test. Such techniquesprolong the time for identifying the best course of therapy forcritically ill patients. And because light scattering is a grossapproach to estimating bacterial mass, a typical McFarland-based test isnonlinear. Nonlinearity is due in part to errors that arise becausevolumetric turbidity tests cannot discern between dead cells and viableones. Yet, scientists generally treat such test data as linear. Inaddition, McFarland-based tests do not distinguish multiple species orstrains of microorganisms in a single patient sample. Properidentification of the members of polymicrobial populations ofmicroorganisms in patient blood samples can be important in the accuratediagnosis and treatment of bacteremia and sepsis.

These and other drawbacks of known counting procedures are overcome bythe disclosed instrument system. This system is designed to accuratelyand precisely quantitate bacterial concentrations in a patient sampleby 1) quantifying the relative abundance of viable microbial cells and2) using that quantification, dynamically calculating the dilutionfactor needed to initiate antimicrobial testing. One beneficial aspectof the disclosed system, which reduces the time required to tailor atreatment to the specific microbe (e.g., bacterium), infecting apatient, is its ability to accept a larger sample concentration inputrange than other antimicrobial susceptibility test systems.

The process starts with the most concentrated amount of a specimen athand, from which one or more aliquots are diluted as needed. The processfollows this conservative approach as it is easier to further dilute asample into the target range than it is to concentrate samples that havebeen diluted past the lower end of the target range. The objective is toobtain the most concentrated sample possible that will work within thedisclosed instrument/device. Detecting a sample's microbialconcentration and diluting it to a target concentration can beaccomplished by known methods, albeit rather slowly and somewhatinaccurately. But dynamically diluting a microbial (e.g., bacterial)inoculum to achieve an appropriate range for accurate susceptibilitytesting of a given organism during each run is a unique aspect of thedisclosed system.

Certain prior art instruments have a specified, pre-determinedconcentration range (set a priori) that is targeted for sample dilution.For example, one known system requires growing colonies on a plate,picking them, and placing the colonies in a tube to establish a cultureof the bacterial isolate. Samples are mixed with a diluent to achieve asuspension, and then inserted into a spectrophotometer to obtain aconcentration reading. For such systems, the lab technician must acquirea densiometric reading in a narrow range, such as 0.46-0.54 (less thanabout 300 CFU (×10⁶/mL)), to estimate the number of colony forming unitspresent (e.g., using a McFarland scale). If both dead and live bacterialcells are present, the final dilution of the sample will not accuratelyreflect the number of viable cells present in the sample forantimicrobial sensitivity testing. Moreover, depending upon the amountof time between removing an initial aliquot of sample and the output ofthe estimated bacterial cell concentration, the population of bacteriamay have doubled one or more times, thereby introducing further errorinto the concentration calculus.

By contrast, translating a cell growth reading into a susceptibilityreport and/or MIC figure using the disclosed system is possible withoutthe preliminary plating step. The disclosed system utilizes a targetrange of 10-130 clones per field of view (such as about 10-100, 20-80,30-60, 10-50, 40-80, or 50-100 clones per field of view) to achieveoptimal bacterial growth. In particular examples, about 20-80 (such asabout 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, or 80) growingclones per field of view is the target range. If too few clones arepresent, then statistically more fields must be viewed to detectbacteria, if they can be detected at all. Too many microbial clones in afield causes crowding and growth inhibition.

For monomicrobial samples, the ideal dilution point of a bacterialsample destined for AST evaluation can be the midpoint of the upperlimit and the lower limit of the targeted range (e.g., target number ofclones per field of view) of the bacterial inoculum. If an inoculumsample is polymicrobial, for example containing a mixture of bacterialspecies or strains, then two paths may be followed for obtaining theideal dilution point. One option is to dilute the samples such that thebacterial counts of both species (e.g., bacterial species A and B) arecentered between the upper and lower limits of the inoculum. A secondoption is to dilute the sample such that the amount of one bacterialspecies (for example, species A) is diluted to the midpoint of theinoculum upper and lower limits, which dilutes bacterial species B to apoint below the lower limit of the inoculum. Under these circumstances,antimicrobial susceptibility testing for bacterial species B is notinitiated, and culturing or a second run would be required to quantitatethe amount of species B present in the sample.

Thus, the disclosed system uses a process superior to McFarland-basedturbidity methods typically used in the clinical laboratory in order toobtain the concentration of bacteria needed for MIC assessment. Thedetection system and staining process together comprise the dynamicdilution aspect of the disclosed system. One skilled in the art willrecognize that the disclosed dynamic dilution method can be used withother systems and methods (such as other systems for detecting ormeasuring microorganisms), in addition to those disclosed herein.

As discussed below, at least in some cases, an effective dilution curvefor a sample can be non-linear. Therefore, the disclosed system uses oneor more subsamples of a patient sample (such as 1, 2, 3, 4, 5, or moresubsamples) to estimate a non-linear effective dilution curve for asample. The estimated non-linear dilution curve is then used todetermine a target dilution factor for diluting the sample to achievethe target number of clones per field of view prior to antimicrobialsensitivity testing.

FIG. 102 is a flowchart of an example method 10200 of determining atarget dilution factor using dynamic dilution and diluting a sampleusing the target dilution. The different process blocks described belowfor FIG. 102 and FIG. 103 can be accomplished using the hardwarecomponents shown in FIG. 101. Additionally, such a method can be usedwith the disclosed methods and systems, or can be used with otherantimicrobial detection and AST systems and methods.

Thus, in some embodiments, at 10202, cell concentrations are determinedfor multiple subsamples of a sample, wherein each subsample is dilutedusing a different dilution factor. In some embodiments, the dilutionfactors include one or more of 0.04, 0.2, and 0.625 (or 25-fold, 5-fold,and 1.6-fold) from a post-GEF sample. However, one will recognize thatadditional or different dilution factors (including, but not limited to2-fold, 3-fold, 10-fold, or 20-fold) can also be used in the methodsdisclosed herein. The multiple subsamples can be extracted from thesample and stored separately from the remaining sample. For example, aninstrument pipettor can be used to extract the subsamples from a samplevial and to place in separate flowcells of a cassette. In someembodiments, at least three subsamples (such as at least 3, 4, or 5subsamples) are used in the dynamic dilution process. When thesubsamples are placed in flowcells of a cassette, a trade-off can existbetween storage space and estimation accuracy. Using a higher number ofsubsamples can lead to a more accurate model of the effective non-lineardilution curve for the sample. However, a higher number of subsamplestake up more flowcells on the cassette. In certain cases, a range ofthree to five subsamples can provide a good balance between accuracy andavailable flowcells on the cassette.

Each subsample is diluted using a distinct sample dilution factor, e.g.,no two subsamples are diluted using the same dilution factor. In someembodiments, the sample dilution factors are preselected to producediluted subsamples that are representative of a range of possible sampleconcentrations. In some embodiments, sample dilution factors areselected such that a target cell concentration is likely to fall withina range created by the diluted subsamples.

After the subsamples are diluted using the sample dilution factors, acell concentration of each diluted subsample is determined. Determininga cell concentration can comprise counting a number of cells in adiluted subsample. In particular non-limiting embodiments, cell numberis determined by contacting each subsample with a universal nucleic acidstain (such as acridine orange or propidium iodide) and counting thenumber of cells. In some examples, the cells are imaged and cellcounting is determined from the image. In a particular example,determining a cell concentration of a subsample comprises usingelectrokinetic concentration to produce a migration of cells in thesubsample toward a surface (e.g., a glass surface of a cassette) priorto staining, optionally imaging, and counting the cells in thesubsample. The cell concentration for a subsample can be a ratio betweena number of cells in a diluted subsample and a volume of fluid in thediluted subsample.

After the cell concentrations of the subsamples have been determined, at10204 a non-linear dilution curve is approximated using the subsampleconcentrations and the associated dilution factors. Approximating thenon-linear dilution curve can comprise creating dilution test points forthe subsamples, wherein each dilution test point comprises one of thesubsample cell concentrations and the corresponding sample dilutionfactor that was used to dilute the subsample. The dilution test pointscan represent points on a non-linear effective dilution curve in atwo-dimensional space, wherein dilution factor is one dimension andresulting cell concentration is the other dimension.

In some embodiments, the non-linear dilution curve is estimated byinterpolating between the dilution test points. For example, thedilution curve can be estimated by performing multiple linearinterpolations and/or multiple spline interpolations between thedilution test points. The non-linear dilution curve can be estimated bycreating a model of the curve. In some embodiments, a model of thenon-linear dilution curve can be created using multiple interpolationsbetween the dilution test points.

In a particular example, a model of the dilution curve is created usinglinear interpolations between multiple dilution test pointscorresponding to multiple subsamples. In one example utilizing threesubsamples, the following formula can be used to create the model:

$D_{T} = \left\{ \begin{matrix}{\frac{C_{1}D_{1}}{C_{T}},{{{for}\mspace{14mu} C_{T}} \leq C_{1}}} \\{{\left\lbrack {C_{T} - C_{1} + {\left( \frac{D_{2}^{- 1} - D_{1}^{- 1}}{C_{2} - C_{1}} \right)D_{1}^{- 1}}} \right\rbrack^{- 1}\left( \frac{C_{2} - C_{1}}{D_{2}^{- 1} - D_{1}^{- 1}} \right)},{{{for}\mspace{14mu} C_{1}} < C_{T} < C_{2}}} \\{{\left\lbrack {C_{T} - C_{2} + {\left( \frac{D_{3}^{- 1} - D_{2}^{- 1}}{C_{3} - C_{2}} \right)D_{2}^{- 1}}} \right\rbrack^{- 1}\left( \frac{C_{3} - C_{2}}{D_{3}^{- 1} - D_{2}^{- 1}} \right)},{{{for}\mspace{14mu} C_{T}} \geq C_{2}}}\end{matrix} \right.$wherein D_(T) is a target dilution factor for the sample, C_(T) is atarget cell concentration for the sample, C₁ is a determined cellconcentration for a first subsample, D₁ is a first sample dilutionfactor associated with C₁ in a first dilution test point, C₂ is a seconddetermined cell concentration for a second subsample, D₂ is a secondsample dilution factor associated with C₂ in a second dilution testpoint, C₃ is a third determined cell concentration for a thirdsubsample, and D₃ is the third sample dilution factor associated with C₃in a third dilution test point. In this example, C₁<C₂<C₃. Using linearinterpolations between three dilution test points is sometimes referredto herein as a “3-point dilution.” Although this particular example usesthree subsamples, and therefore three dilution test points, othernumbers of subsamples and test points are also possible.

In at least one embodiment, creating the model of the non-lineardilution curve comprises determining an average cell concentration usinga proportionality constant. The average cell concentration can bedetermined by multiplying each subsample cell concentration by itscorresponding sample dilution factor raised to a power of aproportionality constant, summing the products, and dividing the sum bya count of the subsamples. In a particular example, the followingformula can be used to determine an average cell concentration:

$C_{avg} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{C_{i}D_{i}^{X}}}}$wherein C_(avg) is the average cell concentration, N is a number of thesubsamples, C_(i) is a determined cell concentration for a subsample i,D_(i) is a corresponding sample dilution factor for subsample i, and Xis a proportionality constant with a value less than 1 and greater than0. The value chosen for the proportionality constant can be an importantfactor in accurately estimating the non-linear dilution curve. In someembodiments, the proportionality constant is a value less than 1 andgreater than or equal to 0.5. In some cases where cells are captured ona two dimensional surface, a proportionality constant of 0.75 can beeffective. A value for the proportionality constant can be determinedempirically by experimenting with multiple possible values and comparingexpected cell concentrations with observed cell concentrations.

At 10206, the approximation of the non-linear dilution curve is used,along with a target cell concentration for the sample, to determine atarget dilution factor. The target cell concentration can be a cellularconcentration for the sample that is conducive to performingantimicrobial susceptibility testing. In some cases, a range of possiblecellular concentrations are acceptable. In such cases, the target cellconcentration can be a selected value within the range of acceptablevalues, such as a midpoint.

In embodiments where the approximation of the non-linear dilution curvecomprises a model of the curve created using multiple interpolations,one of the interpolations can be used to determine the target dilutionfactor. For example, determining the target dilution factor can compriseidentifying a first of the multiple dilution test points with asubsample cell concentration that is less than the target cellconcentration, identifying a second of the multiple dilution test pointswith a subsample cell concentration that is greater than or equal to thetarget cell concentration, and using an interpolation between the firstand second identified dilution test points to determine the targetdilution factor. In cases where the multiple interpolations are linearinterpolations, using the interpolation between the first and secondidentified subsample comprises using a linear interpolation between thetest points. In a particular example, the following formula can be usedto determine the target dilution factor:

$D_{T} = {\left\lbrack {C_{T} - C_{1} + {\left( \frac{D_{2}^{- 1} - D_{1}^{- 1}}{C_{2} - C_{1}} \right)D_{1}^{- 1}}} \right\rbrack^{- 1}\left( \frac{C_{2} - C_{1}}{D_{2}^{- 1} - D_{1}^{- 1}} \right)}$In this example formula, D_(T) is the target dilution factor, C_(T) isthe target cell concentration for the sample, C₁ is the cellconcentration of the first identified dilution test point, D₁ is thecorresponding dilution factor for C₁, C₂ is the cell concentration ofthe second identified dilution test point, and D₂ is the correspondingdilution factor for C₂.

In cases where the target cell concentration is less than a lowest ofthe subsample cell concentrations, determining the target dilutionfactor can comprise using a ratio between a product of the lowestsubsample cell concentration and its corresponding dilution factor andthe target cell concentration. The following example formula isillustrative of such a ratio:

$D_{T} = \frac{C_{1}D_{1}}{C_{T}}$In this example formula, D_(T) is the target dilution factor, C_(T) isthe target cell concentration for the sample, C₁ is the lowest subsamplecell concentration, and D₁ is the corresponding dilution factor for C₁.

In embodiments where the approximation of the non-linear dilution curvecomprises a model of the dilution curve comprising an average cellconcentration and a proportionality constant, determining the targetdilution factor can comprise determining a ratio between the averagecell concentration and the target cell concentration, and then raisingthe ratio to a power of a reciprocal of the proportionality constant. Ina particular example, determining the target dilution factor comprisesusing the following formula:

$D_{T} = \left( \frac{C_{avg}}{C_{T}} \right)^{X^{- 1}}$wherein D_(T) is the target dilution factor, C_(T) is the target cellconcentration for the sample, C_(avg) is the average concentration, andX is the proportionality constant.

At 10208, a portion of the sample is diluted to the target concentrationusing the target dilution factor. In some cases, the portion of thesample is the remainder of the sample that is left after the subsamplesare extracted. In some cases, only part of the remaining sample isdiluted for antimicrobial susceptibility testing and the rest of thesample is left undiluted for other purposes.

In some examples, cell population in a sample can continue to increasewhile the target dilution factor is being determined. If this continuedcell growth is not accounted for, then diluting the sample using thetarget dilution factor can result in a diluted sample with a cellconcentration different from the target cell concentration. In someembodiments, the target dilution factor can be adjusted to account forcell growth in the sample while the target dilution factor is beingdetermined. Additionally, a portion of the cells counted in thesubsample cell concentrations may be non-viable cells. Such non-viablecells may be included in the approximation of the estimate of thenon-linear dilution curve. However, these non-viable cells will notcontribute to growing cell concentrations.

A growth factor can be used to adjust a determined target dilutionfactor to account for cell growth in the sample. The growth factor canalso adjust the target dilution factor to account for a rate ofnon-viable cells, for example a rate of non-viable cells associated withthe identified species. Microbial cell viability is generally accountedfor by developing an assay that maximizes the health of the sampleregardless of what type of microorganism is in it. Nonetheless, sometargets will produce more viable growing clones as a proportion of thetotal number of microorganisms than others. For example, a particulartargeted bacteria might have a growth rate that would cause a doublingin number during the period in which the dynamic dilution occurs.However, if only 90% of those bacteria are viable, then less than 100%of the bacterial population are actually functional and divide forgrowth purposes. The objective is to only count these viable cells asgrowing clones in the flowcell channel. Growth factors can be determinedusing data across multiple runs and many isolates per target group (forexample 10, 50, 100, or more samples per growth factor). Because thegrowth factor is determined empirically, it is ultimately a factor ofthe timing of the assay itself, the health of the bacteria during theprocess of sample preparation and dilution, the growth rate for theindividual target, and the growth “efficiency” of the individual target(non-growing clones/growing clones).

FIG. 103 is a flowchart of an example method 10300 of determining atarget dilution factor for a sample using a growth factor. At 10302, agrowth factor associated with a microorganism in the sample isidentified. The growth factor can be a single factor that accounts forboth a growth rate of the microorganism and a rate of nonviable cells.

Different species of microorganisms can be associated with differentgrowth factors. Table 1 lists example growth factors associated withvarious species of microorganisms. Identifying a growth factorassociated with a microorganism in the sample can comprise identifying aspecies of the microorganism in the sample and selecting a predeterminedgrowth factor associated with the identified species. The growth factoris used to adjust the determined target dilution factor, for example, totake into account cell growth in the sample while the target dilutionfactor is being determined. The growth factor is determined empiricallyfor each microbe (or combination of microbes) being tested. In someexamples, the growth factor is determined for a particular microbe orcombination of microbes by selecting a median of the ratio of the actualnumber of growing clones and the intended number of clones, determinedfrom multiple experiments where a sample is diluted to a dilutionexpected to produce the target number of clones.

TABLE 1 Example growth factors for microorganisms. Microorganism(s)(FISH ID) Growth Factor Proteus vulgaris, Proteus mirabilis (PRO) 0.75Acinetobacter baumannii (ABA) 1 Enterobacter aerogenes, Enterobactercloacae (ENT) 0.78 Enterococcus faecalis (EFS) 0.86 Enterococcus faecium(EFM) 0.75 Klebsiella oxytoca, Klebsiella pneumoniae(KLE) 0.93Pseudomonas aeruginosa (PAE) 0.57 Staphylococcus lugdunensis (SLU) 1.21Staphylococcus aureus (SAU) 1.15 Coagulase negative staphylococcus (CNS)0.86 Citrobacter freundii, Citrobacter koseri (CIT) 1.11 Serratiamarcescens (SMA) 1.3 Escherichia coli (ECO) 0.99

At 10304, at least three subsamples of the sample are diluted usingdifferent sample dilution factors. The dilution factors can be similarto the dilution factors described above with respect to example method10200. At 10306, cell concentrations are determined for each of thethree or more diluted subsamples. Any of the example methods fordetermining cell concentrations described herein can be used todetermine the concentrations of the diluted subsamples.

At 10308, a model of a non-linear dilution curve is created using the atleast three determined cell concentrations and the at least three sampledilution factors. Techniques used to create the model of the non-lineardilution curve can be similar to at least some of the techniquesdescribed above with respect to example method 10200, such asinterpolation between multiple dilution test points, determining anaverage cell concentration using a proportionality constant, etc. Insome embodiments, the model of the non-linear dilution curve can beadjusted to account for the growth factor. The growth factor is selectedbased on the identity of the microbe, which in some examples can bedetermined by the FISH assays described herein. In a particular example,where the model of the non-linear dilution curve comprises linearinterpolations between three dilution test points, the followingadjusted formula can be used to account for the growth factor:

$D_{T} = \left\{ \begin{matrix}{\frac{C_{1}D_{1}G}{C_{T}},{{{for}\mspace{14mu} C_{T}} \leq {GC}_{1}}} \\{{\left\lbrack {\frac{C_{T}}{G} - C_{1} + {\left( \frac{D_{2}^{- 1} - D_{1}^{- 1}}{C_{2} - C_{1}} \right)D_{1}^{- 1}}} \right\rbrack^{- 1}\left( \frac{C_{2} - C_{1}}{D_{2}^{- 1} - D_{1}^{- 1}} \right)},{{{for}\mspace{14mu}{GC}_{1}} < C_{T} < {GC}_{2}}} \\{{\left\lbrack {\frac{C_{T}}{G} - C_{2} + {\left( \frac{D_{3}^{- 1} - D_{2}^{- 1}}{C_{3} - C_{2}} \right)D_{2}^{- 1}}} \right\rbrack^{- 1}\left( \frac{C_{3} - C_{2}}{D_{3}^{- 1} - D_{2}^{- 1}} \right)},{{{for}\mspace{14mu} C_{T}} \geq {GC}_{2}}}\end{matrix} \right.$wherein, G is the growth factor, D_(T) is the target dilution factor,C_(T) is the target cell concentration, C₁ is a first determined cellconcentration for a first subsample, D₁ is a first sample dilutionfactor associated with C₁, C₂ is a second determined cell concentrationfor a second subsample, D₂ is a second sample dilution factor associatedwith C₂, C₃ is a third determined cell concentration for a thirdsubsample, D₃ is a third sample dilution factor associated with C₃.

At 10310, a target dilution factor is determined for the sample usingthe model of the non-linear dilution curve, the growth factor, and atarget cell concentration for the sample.

In embodiments where the approximation of the model of the curve iscreated using interpolations between dilution test points, determiningthe target dilution factor can comprise identifying a first of thedilution test points with a subsample cell concentration that, whenadjusted by the growth factor, is less than the target cellconcentration; identifying a second of the dilution test points with asubsample cell concentration that, when adjusted by the growth factor,is greater than or equal to the target cell concentration, and using aninterpolation between the first and second identified dilution testpoints to determine the target dilution factor and adjust the targetdilution factor using the growth factor. In a particular example, thefollowing formula can be used to determine the target dilution factorand adjust it using the growth factor:

$D_{T} = {\left\lbrack {\frac{C_{T}}{G} - C_{1} + {\left( \frac{D_{2}^{- 1} - D_{1}^{- 1}}{C_{2} - C_{1}} \right)D_{1}^{- 1}}} \right\rbrack^{- 1}\left( \frac{C_{2} - C_{1}}{D_{2}^{- 1} - D_{1}^{- 1}} \right)}$wherein G is the growth factor, D_(T) is the target dilution factor,C_(T) is the target cell concentration for the sample, C₁ is the cellconcentration of the first identified dilution test point, D₁ is thecorresponding dilution factor for C₁, C₂ is the cell concentration ofthe second identified dilution test point, and D₂ is the correspondingdilution factor for C₂.

In cases where the target cell concentration is less than a lowest ofthe subsample cell concentrations adjusted by the growth factor,determining the target dilution can comprise determining a product ofthe lowest subsample cell concentration, the dilution factorcorresponding to the lowest concentration, and the growth factor. Aratio between the determined product and the target cell concentrationcan then be used as the target dilution factor. The following exampleformula is illustrative of such a ratio:

$D_{T} = \frac{C_{1}D_{1}G}{C_{T}}$In this example formula, G is the growth factor, D_(T) is the targetdilution factor, C_(T) is the target cell concentration for the sample,C₁ is the lowest subsample cell concentration, and D₁ is thecorresponding dilution factor for C₁.

In embodiments where the model of the dilution curve comprises anaverage cell concentration determined using a proportionality constant,determining a target dilution factor, and adjusting it using the growthfactor, can comprise adjusting the average concentration using thegrowth factor, determining a ratio between the adjusted average cellconcentration and the target cell concentration, and raising the ratioto a power of a reciprocal of the proportionality constant. In aparticular example, determining the target dilution factor comprisesusing the following formula:

$D_{T} = \left( \frac{{GC}_{avg}}{C_{T}} \right)^{X^{- 1}}$wherein G is the growth factor, D_(T) is the target dilution factor,C_(T) is the target cell concentration for the sample, C_(avg) is theaverage concentration, and X is the proportionality constant.

In any of the example dilution methods described herein, the dilution ofthe sample can be validated during a subsequent antimicrobialsensitivity testing. For example, a growing cell density of the dilutedsample can be quantified in a growth control channel duringantimicrobial sensitivity testing. If a final growing clone density ofthe diluted sample is outside an acceptable range, then theantimicrobial sensitivity testing can be aborted.

Any of the example dilution methods described herein can be performed bya system comprising a system controller. The system controller comprisesa processor and a computer-readable storage medium. Thecomputer-readable storage medium can store instructions that, whenexecuted by the processor, cause the system controller to perform any ofthe dilution method described herein. In a particular example, thesystem comprises disclosed instrument (e.g., instrument 100 in FIG. 1)with an attached system controller operable to control the instrument.

Another aspect of the disclosed system is its ability to account formorphology changes of microorganisms during antibiotic susceptibilitytesting. Some bacteria change morphology during the first few hours ofantibiotic exposure during growth in nutrient medium. For example, somePseudomonas and Enteric bacteria species elongate and become filamentousduring the first few hours of growth in the presence of beta-lactamantibiotics. This morphology shift toward larger bacterial cells may beperceived by automated microscopy systems as growth in the presence ofsuch antibiotics. Under these circumstances, the bacteria would beerroneously characterized as antibiotic resistant by the automatedsystem, even though the bacteria eventually may lyse and die in responseto the antibiotic. This produces a challenge for an automated, rapid ASTsystem designed to determine in less than a few hours whether abacterial isolate is truly resistant to a given antimicrobial drug. Thestandard process for addressing this issue is to simply allow thebacteria to grow in culture for an extended period of time, therebysacrificing the efficiency of antibiotic susceptibility assessment inthe determination of minimal inhibitory concentrations of theantibiotics tested.

The bacterial system addresses this problem by employing an innovativemedium formulation containing a reduced level of nutrient componentscompared to standard growth media. This is counter to standard thinking,as nutrient depletion typically would retard growth of bacterial cellsand therefore hasten their death. For example, significantly reducingthe solute concentration of Mueller-Hinton agar creates an efficienttesting system for differentiating true antibiotic-resistant bacterialcells from susceptible bacterial cells that pass through a temporaryfilamentous phase before dying. Truly resistant bacterial cells continueto grow for up to about 4-5 hours under such nutrient-depletedconditions, whereas susceptible bacterial cells undergo a short periodof elongation and then lyse within the first 1-2 hours of antibioticexposure.

In a similar vein, certain bacterial species, such as Serratia, exhibitdelayed or slower growth patterns under standard AST conditions.Likewise, fastidious bacterial species such as Streptococcus typicallyrequire special nutrient media containing blood components to grow in anAST system, and grow slowly without blood enrichment. Without propergrowth conditions, these bacteria are difficult to test for antibioticsusceptibility, particularly in automated systems designed to produceresults in less than 5 hours. Therefore, a novel media was developed forenhanced antibiotic susceptibility testing with bacterial species thatdemonstrate fastidious or delayed growth patterns under standard cultureconditions. The novel medium formulation combined peptone ingredients inspecific proportions without added blood to support the growth ofSerratia in the presence of antibiotics and fastidious Streptococcusduring AST evaluations for establishing minimal inhibitoryconcentrations for bacterial isolates from patient samples. This newculture medium demonstrated excellent results, enabling rapidsusceptibility testing of problematic organisms/antimicrobialcombinations to yield minimal inhibitory concentration andcategorization (SIR) results.

EXAMPLES Example 1

The disclosed system performs a rapid microbial counting assayimmediately before commencing antimicrobial susceptibility testing(AST). This requires calibration across multiple dilutions. Anequivalent McFarland-based test using FISH takes too much time tocomplete (at least one hour or more). The disclosed bacterial countingprocedure begins by subjecting a sample containing (or suspected ofcontaining) microorganisms to gel electrofiltration (GEF) tosubstantially remove debris. Then, in some instances, the sample ismixed with L-DOPA and introduced into microfluidic flowcells. In thepresence of an electrical potential and a poly-1-lysine coated flowcellchannel surface, sample bacteria adhere to the poly-1-lysine coatedsurface in the chamber. Thus, the captured bacteria are concentratedonto the flowcell surface via electrokinetic concentration (EKC).Unbound bacteria are washed from the flowcell, and then a dynamicdilution (discussed above) is performed. Next, the bacterial sample issubjected to treatment with one or more permeabilizing agents, such asalcohol, an enzyme, a detergent, and the like. For example, 80% ethanolis pipetted into the channel to permeabilize the bacteria present whensmall molecular sized stains (e.g., propidium iodide) are used. Whenlarger sized permeant molecules are utilized (for example, with certainGram-positive organisms), bacteria may be treated with a combination ofpermeabilizing agents, such as ethanol and one or more enzymes. Thepermeabilization process enables stain molecules to enter the bacterialcells for visualization as discussed above. A nucleic acid stain (suchas 1 μg/mL propidium iodide, PI) is used, which intercalates intobacterial DNA upon washing over the bacteria captured on thepoly-1-lysine surface. The sample is then illuminated at an appropriateexcitation wavelength (e.g., 535 nm for PI) and bound stain fluoresces(e.g., for PI, green at an emission maximum of 617 nm). The automateddetection component of the disclosed system quantifies the signalemitted from stained bacterial cells. This is achieved by overlayingdark field images of the field of view, which permits visualization ofcells and debris not present in green fluorescent laser images ofstained cells. The signal cutoff is 5-fold signal over background,thereby eliminating from the count any auto-fluorescence emanating fromdebris.

The concentration of bacteria in the channel is back-calculated toestablish a total amount of bacteria in the sample. If the calculatedamount is, for example, 60 clones, then the sample is within the middleof the target range necessary to get an accurate viewing of bacteriaupon dilution. If the calculated amount is too high (for example, 3,000clones), then the sample must be diluted to a range of 40-80 clones. Theobjective is to hit the middle of this target range in each channel forevery run.

As graphically shown in FIG. 83, a comparison of calibration curves forthe output range for Klebsiella pneumoniae highlights the effect ofusing a multipoint, non-linear curve for quantitating bacteria fordynamic dilution versus a simple, single point linear curveapproximation. In a perfect serial dilution, a sample having a relativeconcentration of 1 (set forth on the x-axis) should theoretically have arelative concentration of 0.1 following a 1:10 dilution, which wouldplot as a straight line. However, when multiple dilution points areactually measured and the resulting concentrations plotted, it becomesclear that Klebsiella pneumoniae (like most other microorganisms) doesnot follow a linear progression at lower concentration ranges. Ratherthe curve is biased toward the left, and there are far more bacteriaactually present than would be predicted from a single point lineardilution curve. Nonetheless, this non-linear curve (labeled as“Klebsiella pneumoniae”) is also not completely accurate, as it does nottake into account the fact that bacteria in the patient sample will bereplicating during the period in which the dilutions and concentrationcalculations transpire. Thus, the disclosed system empirically appliesthe identification information obtained prior to the sample dilutionstep to factor a growth constant into the concentration calculus. Thedifference for Klebsiella pneumoniae is that the “Klebsiella pneumoniae”concentration curve plotted without the growth constant, is lower thanthe true concentration curve (labeled as “Klebsiella pneumoniae with 20%growth factor”).

FIGS. 84A-D further demonstrate this principle in the form of a normalprobability plot comparing the effect of sample dilution of simplelinear dilution curves compared to three-point dilution curves utilizinga growth factor specific for each of the four types of bacteria tested(Acinetobacter baumannii—FIG. 84A, Pseudomonas aeruginosa—FIG. 84B,Klebsiella pneumoniae—FIG. 84C, and Serratia marcescens—FIG. 84D). Eachplot summarizes approximately 1500 experiments. The three-point curvesdemonstrate that dilution merely shifted the curve to the left along thex-axis, but did not discernably alter the lower end of the curve. Bycontrast, the simple linear curves demonstrate that dilution not onlyshifts the curve to the left, but also drives the lower range downwardforming a tail, leading to inaccurate concentration estimations.

In sum, linear dilution curves or curves that fail to account formicrobial (e.g., bacterial) growth rates will under-report the actualconcentration of the patient sample. This error directs the user tounder dilute the aliquot of patient sample destined for antimicrobialsensitivity testing, which results in samples that are too concentratedbeing used in that phase of evaluation. Because the sample will not bediluted enough, too many colonies will be present in a test chamber(flowcell) and will impair the growth of those colonies in the presenceof antimicrobial agents. This obscures the true effect of a givenantimicrobial agent on the growth of the bacteria at issue. Ultimately,the appropriate therapeutic may not be selected for patient treatment asa consequence of faulty AST data. In contrast, utilizing the dynamicdilution described herein, results in more accurate estimation of cellnumber in a sample and therefore, more accurate dilution of the sampleto obtain a target number of colonies in a flowcell (such as about 20-80colonies per field of view). Thus, the disclosed system improves theprecision and accuracy of the AST process over traditional methods knownin the art.

Example 2

A series of growth media was formulated with 0.5%, 0.25%, and 0.125%strength of standard Mueller-Hinton nutrients (3 g beef infusion, 17.5 gcasein hydrolysate, 1.5 g starch, the final product being pH adjusted toneutral at 25° C.) while maintaining the agar concentration at 0.94% inthe culture media. The effect of this depleted-nutrient mediaformulation on bacterial cell growth was evaluated with more than 50strains of Pseudomonas in the presence of a panel of beta-lactamantibiotics. Cells were imaged using automated microscopy as describedabove. Exemplary results are shown in FIGS. 85A-F, in which Pseudomonasstrains were grown in nutrient-depleted media in the presence of apiperacillin/tazobactam antibiotic combination FIGS. 85A-B demonstratesthe effect the antibiotic combination on bacterial growth in a mediumhaving an approximately 87% reduction in Mueller-Hinton nutrients,compared to growth in standard Mueller-Hinton media as shown in FIG.85C. FIGS. 85A-C plots the log of bacterial dark phase intensity versustime, which is proportional to measuring cell mass in culture. FIGS.85D-F are a quantitative representation of the data presented in panelsFIGS. 85A-C expressed as rate of bacterial cell division.

The light colored line in FIG. 85C is generally straight over most ofthe time course, indicating normal bacterial replication with a smallbit of variation in the tail end. The light colored lines in FIGS. 85A-Bare curved with a high degree of variability, indicative of impairedreplication. In FIGS. 85D-F, bacterial growth at 2 hours, 3 hours and3.5 hours is depicted graphically. Bacterial cells grown in depletednutrient media (FIGS. 85D-E) demonstrated great variability in thepresence of antibiotics between 2 hours and 3.5 hours of exposure,whereas little variability was exhibited by cells replicating innutrient-rich media (FIG. 85F). Therefore, no discernable antibioticeffect was evident in filamentous cells grown in standard media duringthe relevant time period, even though the bacteria are indeedsusceptible to piperacillin/tazobactam. By comparison, bacterial cellsof the same isolate grown in nutrient-depleted media show a dramaticallydifferent replication pattern, exhibiting substantially impaired growthin the presence of piperacillin/tazobactam within 3.5 hours. The resultsdemonstrate that this nutrient-depleted media is very effective atdifferentiating true, antibiotic-resistant bacterial cells fromfilamentous, antibiotic-susceptible bacterial cells within about 12hours of growth or less (such as 1-4 hours), depending upon the strainof bacterial cell tested. Use of this nutrient-depleted media permitsrapid, accurate determination of antimicrobial susceptibility offilamentous bacteria and assessment of MIC values via the disclosedsystem.

Example 3

To address problems with testing for the slow growing and/or fastidiousbacteria, a novel medium was formulated with cocktails of peptoneproducts in Mueller-Hinton agar. The new medium increased certainnutrient concentrations without the need for blood components as shownin Table 2 below.

TABLE 2 Accelerate Peptone Medium Formulation 1% Phytone Tryptose CAMHBCombine: 110 g Cation Adjusted Mueller- Hinton Broth dehydrated culturemedia 50 g Tryptose 50 g Phytone 5 L 18 M{acute over ( )}Ω water Heat:Heat, then boil 1 min, remove from heat Autoclave: 121° C. for 45minutes 1% Phytone Tryptose MHA Combine: 375 mL 1% Phytone TryptoseCation Adjusted Mueller- Hinton Broth (CAMHB) 3.5401 g Ultrapure agarWeigh: Record weight Autoclave: 121° C. for 45 minutes Re-weigh: Addback volume of water needed to replace weight differential if vapor lossoccurs during sterilization

The new medium formulation was evaluated with at least 50 strains eachof Serratia or Streptococcus in the presence of various antibiotics.Serratia isolates were tested with piperacillin/tazobactam, Aztreonam,Cefepime, and Ceftazidime. For Streptococcus pneumoniae the growthresponse of the isolates along with the following antibiotics weretested, Cefrtiaxone, Erythromycin, Levofloxacin, Penicillin. In thepresence of this new medium, the disclosed system rapidly and accuratelyinformed users of the susceptibility growth patterns of Serratia withoutexperiencing delayed or slow growth typical of this bacteria. FastidiousStreptococcus strains showed healthy growth in the new medium withoutadded blood components and were rapidly evaluated with the disclosedsystem. Thus, the combination of customized phytone tryptoseMueller-Hinton Agar with the disclosed system technologies provides ameans for quickly obtaining the minimal inhibitory concentrations ofvarious antibiotics that are effective against Serratia andStreptococcus species. This customized media approach should aid in theaccurate susceptibility testing of other fastidious microorganisms(including but not limited to Neisseria gonorrhoeae, Bordatellapertussis, Haemohilus influenzae, Campylobacter spp., or Helicobacterspp.).

Example 4

Model Development

The disclosure provides an improvement in fluorescence identification ofunknown microorganisms, particularly a mixture of microorganisms, inpatient samples using a rapid, massively multiplexed automated singlecell microscopy system. Traditional microscopy relies upon subjectivehuman evaluation of microbial specimens when looking though amicroscope. The microscopist identifies microorganisms by assessingobjects in a field of view that are recognizable by shape, size, etc. Anovice microscopist tends to be far less accurate than a highlyexperienced scientist, who—after reviewing many, many specimens—willhave developed a trained eye for recognizing the characteristics ofparticular microorganisms. Likewise, the accuracy of an automated singlecell microscopy system, such as the disclosed instrument system, turnson the ability of that system to recognize features characteristic of agiven microbe. Accurate identification in such a system requires propertraining of the system to reflect the same type of experience acquiredby a seasoned human microscopist. One advantage to using a properlytrained automated microscopy system is the efficiency with which it canaccurately identify microorganisms in multiple samples in a short periodof time.

For automated identification of unknown microbes via FISH, one drawbackis the potential for an unfavorable signal to noise ratio. A substantialamount of non-cellular noise may be present in samples, which canconfound accurate detection of microbes during an automated read of thesamples. In various embodiments disclosed herein, this problem isaddressed by using a universal probe as a control and one or more targetprobes in the same analysis. The control and target probes can be taggedwith different detectable labels that can be co-locally detected on asingle microorganism following excitation at different wavelengths.Thus, for example, in a two-color instrument, the universal controlprobe will indicate that an object is a microbe (e.g., bacterium orfungi) and the second color probe will identify the species of themicrobe. A third color probe (three layer system) enables systemidentification of an object, for example identifying the object as abacterium, its category, and species. Labeled FISH probes may bedesigned to recognize microorganisms in many ways, including—but notlimited to—native and synthetically derived probes that recognize andbind to rRNA sequences in the target organism. Some microbialidentification tests are commercially available, but do not employ aninternal control for each test, which leads to increased levels ofinaccuracy. By contrast, embodiments of the innovative identificationprocess and system disclosed herein provide internal controls for eachindividual identification test.

Variation exists to some degree within most—if not all—aspects ofbiologically-based microbial identification assays. For example,variation may arise between species of bacteria, between bacterialisolates, and within components of a given detection system. Within adetection system, each probe channel may demonstrate signal variation.There may be variable noise, variation from reagents, variation due totemperature shifts, etc., all of which contribute to system variation.Thus, for example, a single bacterial population will exhibit variationand noise within each probe channel of a detection instrument. It isimportant to ensure robust signal detection from such probes, toquantify how many objects are present in a visual field, and then todetermine the confidence with which the probes in question actually bindto target rRNA sequences. In various embodiments, the identificationprocess disclosed herein takes into account this complex mix of sourcesthat can contribute to microbial misidentification. Thus, the disclosedsystem employs probabilistic expectation models of multiple sources ofsignal and noise during the microbe identification process. Thisapproach 1) ensures that what is being detected is not merely anartifact emitting a signal due to non-specific probe interaction, and 2)discriminates between known and unknown microorganisms.

To do so, the disclosed process and system determines how to account forthe milieu of sources of variability that may emanate from a given probechannel. Recognizing and accounting for these variables permits theinnovative identification process disclosed herein to be continuouslyrefined during training to incorporate this variation into multipleprobabilistic expectation models. The disclosed system combines, e.g.,signal, noise (for example, background), and crosstalk (such as bindingof a species-specific probe to non-target species) data points to informthe system of microbial identification parameters with a certain levelof confidence. Expectations are constructed in the model such thatobservations will generate optimal fit. Fitting expectations over anumber of queries is the automated system's equivalent of humanlearning. Thus, the probabilistic expectation model provides a unifiedframework for assigning probability scores for a plurality of eventsthat arise during identification assays.

As it queries relevant events, the disclosed system uses Bayesiantechniques to assign weighted probabilities to each answer receivedbased on prior knowledge acquired during training, and (when applicable)previous sample runs. In essence, the system learns what noise andcrosstalk look like for each target probe and heavily leverages thisknowledge in order to identify a signature signal pattern for eachtarget probe. This combination of bits of pertinent information fordecision making by the system permits the distinction between signal,crosstalk and noise. Even the lack of an apparent target signal canproduce data indicative of the presence of a microorganism, e.g., onethat is not represented by a target probe in the assay panel. A humantechnician, by contrast, would likely not recognize this, as they arebiased toward identifying positive target probe signals, but not able toassimilate the information present in what might otherwise be considerednegative results. The outcome is a high degree of both sensitivity andspecificity not possible with conventional identification processes.

The methods and systems herein place more confidence in producing signalthan parsing out irrelevant data sources (noise, etc.). Accordingly, theidentification process prospectively models noise-connected signals aswell as structure noise (e.g., crosstalk, etc.) because noise can bereproducibly measured and yields superior performance results. Theinventive identification process employs an agglomerative approach todecision making. This decision making methodology subscribes to thenotion that with an increasing number of observations of a givenpopulation, one approaches a finite point for quantifying importantfeatures of objects (e.g., mean, dispersion, etc.). If a snapshot iscaptured of a system, probes, reagents, and the like, all of thesecomponents interact to form the totality of the variability that must berecognized and captured by a robust probabilistic expectation modelbased on signal production. This is accomplished in part by fine tuningthe illumination and detection procedure in the system and matching theexpectation of signal, thereby closing the loop on the systemperformance of agglomeration expectation. Therefore, with repeatedobservations over time, the system enables the prediction of probeperformance. In other words, the variability of an entire system becomesfinite after a large number of observations. Once these parameters areknown and accounted for in a probability model, the identificationprocess increases in accuracy and approaches 100%.

During training, the instrument system crafts a normal distribution ofmicrobes in a reference panel, such as a standard panel of bacterial andfungal probes. An informed distribution pattern acts like a fingerprintspecific for each target organism represented in the panel. If an objectfluoresces during a patient sample run that does not fit modelexpectations, then the system determines that the object is an outlier.In doing so, the system and its operator can tell which experimentsworked and which ones did not. As an example, if the goal is 95%accuracy for an identification system, a microscopist should be able todetermine whether the identification system is working by observingprobe patterns. If the probes are dim, or are not specific, themicroscopist will know based on the expectation model whether the 95%accuracy threshold will be met. And as mentioned above, the trainedsystem can recognize signal patterns that do not fit a microorganismrepresented in the reference panel, but are not simply noise. By using acombination of signals generated by a universal probe, dark fieldimages, and fluorescent expressions in the acridine orange controlchannel, the system can detect the presence of an off-panel microbe in asample. That information can then direct a clinician to test the patientsample against additional probes to identify the off-panel organism.

To develop the inventive probabilistic expectation model, Bayesiananalysis was used to set system accuracy and performance levels. Thus,the total system performance was in view of normal variance and systemcomponents adjusted to reach a 95% accuracy level. More particularly, aBayesian solution is a statement of probability concerning a parametervalue given fixed bounds (data). Bayesian reasoning assesses thelikelihood of a thing being true in view of the evidence at hand, whichis captured in a theorem:p(A|B)=p(B|A)p(A)/p(B)

This equation provides for the probability of A given B (“p(A|B”),wherein the equation calculates the probability of observing event Agiven that evidence B is present. The application of Bayes theorem in,for example, the microbial identification context, directs that the truelikelihood of a microorganism being properly identified in laboratorytesting turns on inputting the right data into the identificationcalculus, as opposed to relying upon intuitive perception. Pursuant toBayesian theory, the methodology begins with existing prior beliefs in agiven subject. These existing beliefs are updated with new informationto formulate “posterior” beliefs, which are subsequently used as afoundation for inferential decisions.

Using this approach, training on a reference dataset enables artifactdetection (bubbles, polymicrobial samples, etc.) to be accomplished by asignal-based probability expectation model. Likewise, the model enablesthe accurate detection of multiple microorganisms in a single sample.Built into the model is the expectation of each organism in a referencepanel and the ability to discriminate expression of an unknown microbein a sample. Thus, the probabilistic expectation model is built on anexpectation for expression of unknown organisms in a patient sample, andcan discriminate their signals from noise. This process incorporatesprobabilistic modeling as feedback for experimental design such thatoverall system performance (via probe selection, etc.) is optimized. Akey aspect of the model is the creation of a relationship by goodestimation of cell counts based on signals, not noise. This isespecially important when trying to unmix multiple bacteria in a sample,especially if the proportion of the bacteria in the sample is unknown.Accordingly, accurate identification of bacteria according to aspects ofthe invention hinge on model driven development, artifactdiscrimination, and model driven performance.

Model Evaluation

In certain embodiments, the disclosed FISH identification algorithmbegins with the development of a large reference dataset from which agiven unknown microbial type can be identified based on predictable,known features or parameters characteristic of that microbe. In FIG.93A, an initial dataset was established in an experimental assayconsisting of 17 bacterial probe flow cells plus an acridine orange (AO)control flow cell applied to a blood sample spiked with known bacteria.These reference samples were processed in standard fashion and subjectedto automated FISH analysis using dark field, green fluorescent (targetmicrobe) and red fluorescent (universal for bacteria and fungi)illumination conditions. Three images were captured per site, with 10 to24 sites per flow cell. The images were processed to identify trueexpression, defined to be signal-over-background in target/universalprobe space per object per flow cell. Large debris, such as bubbles,were identified and discarded from the calculus using dark field imagefeatures. Objects of interest were detected in all three imaging modes,but only those objects that co-localized (e.g., were spatiallyco-occurring) were retained as true objects of interest (i.e., taggedmicroorganisms). FIG. 94 graphically depicts the distribution of signalemissions plotting target probe detection on the x-axis and universalmicrobial probe on the y-axis.

Flow cells containing samples are invariably populated with a milieu ofmaterials in addition to the targeted microbial cells. For example,samples may contain contaminants that include—but are not limitedto—biological debris (such as ruptured cells, partial cell membranes,etc.) and artifacts (such as bubbles, instrument cassette impurities).The sample debris and/or artifacts may generate background noise thatwill skew quantification unless taken into account during the analysisprocess. This sample debris and/or artifacts compete with signalsemitted from targeted microbial cells bound to fluorescently taggedprobes, thereby making it difficult to distinguish images of “true”targets from debris and artifacts. In other cases (depending upon thestructure of the instrument involved), tagged target cells are“light-starved” increasing signal-to-noise variability over the field ofview. Furthermore, flow cell cross-talk may arise, such as throughcross-hybridization between target probes. To confound theseidentification challenges, unknown organisms may be fluorescently taggedby FISH probes due to an overlap of sequences shared with targetorganisms, and these organisms may not be represented in a referencepanel utilized in the experiment. Finally, if a microorganism(s) ispresent in a sample only in very low concentrations (e.g., <10 clonesper field of view), then it may be difficult to achieve confidentdetection of the organism of interest.

Internally spiked blood bottles were used to create reference samplesthat were employed in building signal-mediated probabilistic expectationmodels of labeled bacteria and fungi. Initial model training istypically based on these reference samples. Once the trained models areestablished, expression data obtained from patient samples may be addedto prior observations (“priors”) from the training runs. Doing so modelsthe variability of different bacterial isolates obtained from multipleblood incubation or storage systems. For example, medical treatmentfacilities around the world use an array of different blood bottles forpatient blood incubation, each of which may have slightly differentproperties that may (or may not) ultimately affect bacterialcharacteristics used in the inventive identification algorithmicprocess. This “bottle type variability” has the potential to affectexpression data distribution, and thus it's fit to a given model. Addingthis source of variability to a model after initial model developmentpermits the adaptation of the model to any shift in expression thatbecomes evident in view of patient sampling conditions. In other words,signal-mediated probabilistic expression models may be updated post-hocto include Bayesian information that can modify priors to ensureaccurate probability. In essence, the algorithm permits instruments tomimic the human learning process to generate a teaching system thatconveys what is viewed through the microscope as if viewed by a person.The result is that the error rate decreases with time and thenstabilizes at some point due to data from the large reference populationbeing enhanced by patient sampling data, which is a feature ofagglomerative decision theory, as noted previously.

Accordingly, the process typically begins with quantifying a “normal” orbaseline expression of target microorganisms and/or probes in areference sample subjected to analysis. This aspect of the analysisprocess generates models for use in evaluation of subsequent sampleobservations. After “normal” or baseline models are generated, thecross-talk expression of probes is quantified to account for noise thatwould impair the accuracy of the microbial discrimination/identificationanalysis. Such cross-talk related noise may arise from numerous sources,including—but not limited to—off-target “noise” and background/process“noise.” Quantification of the “normal” expression of target organismstogether with the quantification of cross-talk expression of probespermits generalization for polymicrobial expression interpretation ofsamples. In a rapid, automated single cell identification system, suchas the disclosed system, the identification of microorganisms frompatient samples can be accomplished in about an hour once an aliquot ofis loaded into the disclosed instrument for analysis. The exemplaryEnterobacter distribution panel of FIG. 93A demonstrates that withproper system training and the use of appropriately specific probes, thedisclosed system was able to craft a probability density function(“PDF”) that behaves as an expression “fingerprint” for identifyingEnterobacter bacteria in a patient sample. Thus, whenever the disclosedsystem encounters such distribution, it will identify the presence ofEnterobacter in a patient sample. This is especially important whenidentifying microorganisms in a mixed population (polymicrobial) sample.

General Method Steps:

1. Obtain test sample;

2. Introduce aliquot of sample to vial;

3. Incubate for time sufficient to establish that one or more microbesexist in sample;

4. Transfer aliquot to flow cell;

5. Introduce labeled universal control probe and one or more labeledtarget probes, as well as an acridine orange control stain;

6. Capture images of multiple fields of view of one or more microbes;

7. Perform morphokinetic analysis to identify characteristics of imagedmicrobes;

8. Apply analysis data to probability model to identify microbe usingFISH identification algorithmic process.

In particular, the process further comprises:

-   -   Features per imaged object identified via morphological and        other analysis are processed through Bayesian framework (i.e.,        signal, noise, and crosstalk models) to attain posterior        distributions for each microfluidic flow channel;    -   Posterior distributions are passed through Kernel Density        Estimator (KDE) and integrated to attain a resulting        “likelihood” of an event per microfluidic flow channel; and    -   Off-panel detection logic is invoked to determine the presence        or absence of one or more off-panel microorganisms.

FIG. 93B is a flowchart of an exemplary method for the identificationalgorithm. As shown at 9310, three different images of a same point in aflow cell are captured using different light sources. Image 9312 is adark field image, while images 9314, 9316 are two different fluorescentimages using light sources of different wavelengths. Thus, imageacquisition occurs with at least three modes of illumination. In oneembodiment, different laser diodes can be used to generate thefluorescent signals, while the dark field image can be generated using abroadband LED. The images are input into an image processing stack 9320,which extracts features for a plurality of objects in the images.Example features include shape features, intensity, orientation,fluorescent channel matching, etc. The image processing stack 9320generates an object/features matrix table 9330, which describes each ofthe features found for all objects in the flow cells. Based on thefeatures found, some of the objects can be filtered as known noise,debris, etc. in module 9340.

The remaining objects/features are compared in parallel to multiplemodels, shown generally at 9350. The multiple models 9350 take intoaccount signal, noise and cross-talk information for each of the darkfield and fluorescent imaging wavelengths. The models 9350 are generatedas described below and can be based on past observations of knownmicroorganisms. The models are associated with expectations of featuresattributable to known microorganisms and represent statistical modelsthat take into account variability that arises through observations oflarge populations of cells. Such observations are used to train themodels so that probabilities can be determined from the models duringcell identification. The signal information relates to the intensity oflight from probes attached to the imaged objects. The noise informationcan be defined as background signal, for example, due to binding of adetectably labeled probe or nucleic acid dye to non-nucleic acidcomponents in a sample, such as debris. Noise also includes backgroundsignal in a field of view (for example, fluorescence in a field of viewcontaining a detectably labeled probe but no biological sample). Thecross-talk signal relates to how the presence of other organisms inother channels impacts the organism being identified. The cross-talk canbe associated with non-specific probe binding and is a repeatable signalthat is modeled. More specifically, cross-talk includes hybridization ofa detectably labeled species-specific probe to a non-targetmicroorganism (also referred to as non-specific binding or non-specifichybridization). Cross-talk can occur when a species-specific probehybridizes non-specifically or at a lower stringency (for example, lowerTm) to a non-target microorganism, such as a microorganisms that isrelatively closely related to the target microorganism. As a specific,non-limiting example, an E. coli species-specific probe may alsohybridize to a lesser extent to related microorganisms, such asCitrobacter spp.

The models 9350 are coupled in parallel to a target presence module 9360that computes a probability distribution for signal (S_(i)), noise(N_(i)) and cross-talk (C_(i)) for each object identified. Additionally,module 9360 determines a quantity of objects that meet thresholds forsatisfying each of the signal, noise and cross-talk models. If module9360 determines that there is a low-probability of a signal andcross-talk, then identification for the object of interest has notsucceeded and control is passed to module 9370, which determines anoff-panel presence. The off-panel presence module receives a nucleicacid stain signal (such as an acridine orange control 9372) and candefinitively determine whether or not an organism is present that is noton the panel. If a target is identified in module 9360, off-panelpresence can still be determined at module 9380, which also receives theacridine orange control 9372. The output of module 9380 is theidentified target plus any off-panel presence information, or just thetarget if no further off-panel information is provided.

Thus, image acquisition occurs for at least three modes of illumination,including multiple fluorescent modes and a dark field mode for a singlecell. Additional controls, such as a non-specific nucleic acid stain(for example, acridine orange) image can be used. Imaging a target probewith imaging any other optical control, such as dark field imagingand/or imaging of a universal probe, can be used on a per-cell level inthe identification process described herein. Cell-specific control withmultiple modes of illumination at the same field of view and sameresolution and modeling for signal, noise and cross-talk providesoptimal identification of the cells, as well as definitively concludingthat a cell is present, as well as that a cell is not present. Forexample, one channel can be determined positive for a specificmicroorganism (such as E. coli), but with high certainty othermicroorganisms can be conclusively determined not present. In this way,target information and non-target information, such as noise andcross-talk, all combine to provide positive information to assist inidentification of a microorganism. The individual probabilities of thesignal, cross-talk and noise combine to maximize the probability ofproper identification. An aspect of various embodiments employing theprobabilistic model using Bayesian analysis is the determination of thelikelihood per expression for each probe. The process directs theevaluation of some posterior quality per probe according to the formula:L _(i) ∞P(_(Mi) |{F _(G) ,F _(R)})

The FISH identification algorithmic process uses fairly robust methodsfor building expectations. Identification of organisms can be madeutilizing various characteristics, such as bacteria-specific morphologyfeatures. The characteristic variables measured are not necessarilyorthogonal, thus requiring full Bayesian formulation. The assessment maybe increased by one or two more dimensions, if the additional datapoints will improve microbe discrimination. Population-based metricshave a requirement of a higher minimum clone count per flow cell. In oneembodiment, the overall minimum limit of detection is 12 cells per flowcell. In other embodiments, methods were employed for thresholding at50<count<150 experiments.

Intra-channel “polymicrobial populations” (e.g., samples containing twoor more different microorganisms) can be a confounding factor inidentifying microbial entities in a patient sample. Process and/orfluidics artifacts can lead to sub-populations within a flow cell,meaning that off-panel organisms can produce additional expressingpopulations. Posterior PDF estimates allow for detection of multiplesub-populations (Gaussian Mixture Models or the like are employed). Bydefinition, a Gaussian mixture model is a probabilistic statisticalmodel that assumes all data points pertinent to an analysis aregenerated from a mixture of a finite number of Gaussian distributionswith unknown parameters. In other words, the Gaussian mixture model is asemiparametric approach to density estimation based on the use of amodel that best fits the input data. Various estimations of Gaussianmixture models may be crafted to correspond to different estimationstrategies. A Gaussian Mixture Model object may implement anExpectation-Maximization (EM) or Maximum Likelihood Estimation algorithmfor fitting a mixture of Gaussian models and determining modelparameters. It can also draw confidence ellipsoids for multivariatemodels, and compute the Bayesian Information Criterion to assess thenumber of clusters in the data. A Gaussian Mixture Model learns from atraining data set to find the probability density function of a data setbased on the criteria found in images acquired from the training set.Various techniques may be utilized for editing the reference data set.For example, the mean and standard deviation of each descriptor may beobtained from a large sampling population and then the assumption ofthat the descriptor will be approximated by Gaussian distribution. Inthis case, objects in a sample field of view whose descriptors are lowerthan 3 standard deviations of the mean will be rejected. Thus, themethod can assign to each sample the class of the Gaussian it mostlyprobably belongs to using a Gaussian Mixture Model “predict” method.Gaussian Mixture Models come with different options to constrain thecovariance of the different classes estimated. This class approachallows for easier evaluation of, sampling from, and maximum-likelihoodestimation of the parameters of a Gaussian Mixture Model distribution.

FIG. 93C is a flowchart of a method for identifying a targetmicroorganism. In process block 9385, multiple images are captured usinga camera of an object using different modalities of light. The differentmodalities can include different wavelengths of light. In process block9387, features of the objects identified can be determined. Typically,the hardware components in FIG. 101 are used to perform such adetermination, which can be embedded on the control board 321. Featurescan include shape, orientation, intensity, etc. In process block, 9389,a plurality of models are provided based on signals, noise and/orcross-talk. The models can include only signals and noise or signals andcross-talk. The models can be stored in memory, such as memory 10120 or10125. The models can further be stored in storage 10140. In processblock 9391, a probability distribution is calculated for a signal and atleast one of the noise or cross-talk. The probability distribution canbe computed using the central processing unit 10110 or the graphicsprocessing unit 10115. In process block 9393, a determination is madewhether or not the microorganism is present in at least one of thecaptured multiple images using the calculated probability distribution.Such a determination can be made using the computing environment 10100.

FIG. 94 takes the expression data presented in FIG. 93A and presents itas bivariate PDF patterns for each of the microorganisms tested. Thus,the expression of each microorganism is characterized by the uniquepatterns developed after repeated training and sampling experiments. ThePDF patterns were “learned” by the disclosed system independent of biasand provide a reliable expectation of how these microbes would respondto probes tested against an unknown patient sample. The disclosed systemapplies an empirical threshold value for matching the signal pattern ofan unknown microorganism with the PDF of a panel probe. As is evident inFIG. 94, some of the PDF patterns are highly skewed along the y-axis,whereas others are highly aligned along the x-axis. Dead center of eachellipse is colored deep red, which indicates the location of themajority of signal distribution for each microorganism tested. TheEnterobacter sample (framed in a black rectangle in FIG. 94) shows abalanced Log-Normal distribution.

As previously noted, variability in microbial identification can arisefrom multiple sources, including (but not limited to), probes,instruments, reagents, and the like, which can shift a sample reading tooutlier status. These sources of variability were taken into accountwhen performing test runs while developing the training data set. Testruns were performed on multiple instruments in order to observe normalbetween-instrument variability. Readings outside of normal values wererejected. Initial instrument training began with between approximately50 and 466 isolates per species of microorganism. The second set oftraining runs utilized about 25 isolates per species of microorganism.Training of the automated instrument was undertaken to generate aMaximum Likelihood Estimation (“MLE”) which incorporates values thatmaximize the likelihood function given a set of observed data. Thisapproach applies the principle that the best parameters are those thatmaximize the probability of observing current values in statisticalmodeling. Thus, the MLE was intended to estimate expression patterns offluorescently tagged bacteria and fungi using observational data fromthe reference panel of microorganisms. The primary goal of modeling isto deduce the form of the underlying process by testing the viability ofa model. The model is tested by evaluating the goodness of fit ofobserved data, i.e., assessing how well the model fits the observeddata. Goodness of fit is measured by parameter estimation, which entailsfinding the parameter values of a model that best fit the observed data.

As shown in FIG. 95, multiple test runs of Enterobacter samples wereperformed to generate distribution patterns during instrument training.As with FIG. 93A, the graphs plot target signal distribution on thex-axis against universal probe signal distribution along the y-axis. Thepink spots represent noise which is used in the calculus for modelingsignal. The MLE calculation involved combining the estimated likelihoodof an occurrence being present for the distribution patterns forEnterobacter. A subset of the estimated 466 total experiments used fortraining the instrument were combined to craft the MLE for each probe.Thus, for Enterobacter, for example, the ENT “model” (delineated asMENT) was created. This can be characterized mathematically asmultivariate log-normal function:ƒ(x)=exp(N(μ,Σ))

The combined parameters from the subset of experiments were plotted inFIG. 95 as a black and white image contour of data distribution forMENT. Model evaluation using Bayesian formulation was undertaken.Sampling distribution p(X|θ) is an observation (experimental data) whereX is {Fg, Fr} and θ is the parametric model M (obtained from Training).Prior distribution p(θ) is unknown (assume it equals p1). Thus,posterior distribution of the Enterobacter training (or any givenexperiment) is:

$\left. {{p\left( {M❘\left\{ {{Fg},{Fr}} \right\}} \right)} = {{\frac{{p\left( {\left\{ {{Fg},{Fr}} \right\} ❘M} \right\}}{p(M)}}{p\left( \left\{ {{Fg},{Fr}} \right\} \right)}{{\infty p}\left( \left\{ {{Fg},{Fr}} \right\} \right)}}❘M}} \right)$

where p({Fg,Fr}) is a normalizing constant representing all possibleexpressions over the parameter space involved. Evaluation optionsinclude employing a Maximum-a-Posteriori (“MAP”) probability estimateand/or employing a bounded posterior integration: Li=∫_(a) ^(1.0) PM,where PM is the estimated probability density function of p(M|{Fg, Fr}).By using a Bayesian pathway, the inventive process assigns a probabilityas to whether probe binding by microorganisms in a patient sample willmatch that of known microorganisms in a reference panel. Identificationof pathogens using this process is based on a combination of posteriorprobabilities from multiple flow channels.

Although a probability model can be “finalized” based on a giventraining regimen, the process can continue to monitor microorganismidentification data to augment posterior beliefs of the probes used,permitting continual evaluation of expectations—and thus theidentification models based on those expectations. This provides userconfidence for augmenting the system with new probes to address newlydeveloped variability should it arise. For example, an identificationexpectation model was developed using a reference panel of bacteria andfungi that included 90% of blood pathogens, creating a great knowledgebase. Microscopists using this model know what the majority ofpathogenic blood microbes look like in the inventive system, andprovides confidence that testing for these pathogens using this systemis both sensitive and specific. But there is room for improving theexpectation models should new bacterial strains arise.

Enterobacter modeling is presented as an exemplary case, but theprinciples apply equally to any probe for microbes in the referencepanel. A good match case is illustrated in FIG. 96, in which model fitwas 0.94 (94%). The left upper panel of FIG. 96 is a graphic that showsfluorescing microbial cells detected by the system. As before, thex-axis plots target probe signal distribution, while the y-axis plotsuniversal probe signal distribution. The right panel of FIG. 96 adds acolored probability scale to the graphic, in which 0 indicates noprobability of Enterobacter being present (noise, as depicted by whitedots in the elliptical pattern) and 1.0 representing a 100% probabilityof the presence of Enterobacter (as depicted by black dots in theelliptical pattern). A small tail is evident toward the graph origin,which is noise (not present in dark field) and therefore is not countedtoward signal. Experimental evaluation assessed cell signal againstkernel density, which approached nearly 100% probability. A kerneldensity estimation is a fundamental data smoothing tool useful whereinferences are made about a population in light of a finite data sample.Kernel modeling takes the posterior distribution and provides a PDF,from which a Maximum-a-Posteriori estimate may be derived. Kernelmodeling will scan across an entire histogram and the observation isassigned a value of 1; the PDF must then integrate to 1 to fill in gaps,as shown in the bottom right histogram of FIG. 96. Because theinnovative process uses a kernel density estimation on a posteriordistribution—as opposed to, for example, raw input data—the system cancompare probability for experiments having varying sample populationdensities.

By contrast, FIG. 97 depicts the probability model for a less robustEnterobacter test, which generated a model fit at 0.74 (74%). Here thedata points are fairly confined to a specific region of the expecteddistribution pattern, but are sparse, and therefore gaps exist in theellipse. Under these circumstances, integrating probability with fewercells is a challenge; performing a kernel density estimate aided inovercoming the shortfall. Kernel modeling, as noted above, provided aposterior PDF distribution and a PDF, which is particularly useful whendata points are sparse in a given model. It normalizes for cells byevaluating and filling in gaps assuming local Gaussian distribution.This gap filling essentially permits the genesis of continuous space fordata that needs to be fit to a model. In the context of FISHidentification, the root cause of the scarce data may be due to problemssuch as dimmer isolates or a shift in the entire operating point (highbackground is one dimension, etc.).

A compact model of Enterobacter signal distribution, as shown in FIG.98, has a lower score of 0.64 (64%) than the more robust model depictedin FIG. 95. Nonetheless, the compact model exhibited more bacterial datapoints than were present in, for example, the “scarce’ model of FIG. 97.The distinction lies with the fact that the probability model of FIG. 97has more data points than the “scarce” model, but they are not asbroadly distributed within the confidence space as they are in the“good” model.

Finally, FIG. 99 shows an example of a “not-so-good” case of this typeof modeling, with an MAP estimate of 0.13 (13%). The data points areskewed toward the 0,0 origin and there were not enough points to fillmost of the elliptical patterning space. This may be due to few or evenno target cells being present, meaning that most of the “signal” isactually noise or artifacts.

Example 5

Basic Principles of Multiplexed Automated Digital Microscopy

Multiplexed automated digital microscopy uses a multichannel testcassette and cell immobilization to enable microscopy-based, single-cellanalysis for organism identification in about one (1) hour andantimicrobial susceptibility testing in about five (5) hours directlyfrom clinical specimens. Bacterial and fungal (yeast) cell-by-cellidentification was performed using fluorescence in situ hybridization.Susceptibility reports were generated by digital microscopic observationof individual, live, growing immobilized microbial cells in nearreal-time (approximately every 10 minutes) in the presence ofantimicrobial agents. Antimicrobials for susceptibility testing wereselected based on the organism identification result. Organisms thatwere not identified by a specific FISH assay (non-target organisms) werereported as detected but not identified, and susceptibility testing wasnot performed on these microbes. The technology enables the analysis ofpolymicrobial specimens, and an integrated, automated sample preparationprocess was developed for certain specimen types. The general processflow for a fully automated system is illustrated in FIG. 86.

Automated Sample Preparation—Gel Electro-Filtration (GEF)

Automated sample preparation was performed using a gelelectro-filtration process, which is based on gel electrophoresisprinciples (FIG. 87). Clinical samples were automatically transferred toan agarose gel containing pores smaller than bacterial and yeast cells.The gel was immersed in an electrokinetic buffer that causes bacterialand yeast cells to carry a negative charge. When a voltage was applied,sample impurities such as lysed blood cells and debris passed into thegel, while the larger bacterial and yeast cells remained trapped in thewell. At the end of the process, the voltage was briefly reversed toliberate the bacterial and yeast cells from the wall of the well. Thepurified inoculum was then pipetted into individual flowcells of amultichannel test cassette for cell immobilization and identification orantimicrobial susceptibility testing.

Cell Capture Via Electrokinetic Concentration (EKC)

A multichannel test cassette composed of a transparent glass bottom andplastic top that is molded to form parallel flowcell channels was used.The top and bottom surfaces of each flowcell channel were coated with alayer of conductive indium tin oxide (ITO) that serves as electrodes.The bottom surface has an additional cationic poly-L-lysine layer thatacts as a capture surface. Inoculum was added to the flowcell and a lowvoltage briefly applied that caused negatively-charged bacterial andyeast cells to migrate to the lower surface where they were captured,and ready to undergo identification or antimicrobial susceptibilitytesting (FIG. 88).

Identification by Fluorescence In Situ Hybridization (FISH)

Once cells were immobilized, a FISH assay was performed foridentification. Following permeabilization and washing steps, cocktailsof ATTO-532 (green) fluorescently labeled DNA probe(s) designed to bindto the rRNA of each identification target were added to differentflowcells. Each cocktail also contained an ATTO-647 (red) labeleduniversal microbial probe capable of binding bacterial or yeast cells.The universal microbial probe binds to rRNA of all bacterial and yeastcells, thereby identifying the presence of such cells even if they arenot recognized by target probes. This aids in the detection of off-panelmicroorganisms in biological samples. Each microfluidic flowcell wasimaged using an epifluorescence microscope with a camera at 532 nm, 647nm and in dark-field.

After image collection, custom image analysis software measured thesignal-to-background ratio for each fluorescent and dark-field object ineach microfluidic flowcell. To exclude debris, only dark-field objectsco-localized with universal probe signal were included in the analysis.Co-localization of target probe signal and universal probe signalidentified a target organism (FIG. 89). The software can also be used toquantitate the number of objects in a flowcell. A universal nucleic acidstain (e.g., acridine orange, propidium iodide, or DAPI) is added to anadditional flowcell as a control in order to quantitate the total numberof organisms present per flowcell in the sample. Comparing the relativenumbers of each target organism to objects lit up with universalbacterial, yeast, or nucleic acid probes allowed for the detection ofnon-target organisms and identification of polymicrobial samples.

Antimicrobial Susceptibility Testing (AST)

The results of the identification assay drive antibiotic selection forantimicrobial susceptibility testing. The remaining samples undergo apre-growth step to normalize growth rates during the approximately1-hour FISH ID assay. The concentration of organisms in the purifiedinoculum was determined by repeating the quantitation process with auniversal nucleic acid stain as previously described herein. Based onthese results, additional flowcells were filled with the purifiedinoculum subjected to dynamic dilution to the appropriate target rangefor antimicrobial susceptibility testing. Following cell immobilization,antimicrobial solutions in Mueller-Hinton agar were dispensed into theflowcells. Different antimicrobials were tested in separate flowcells,and only a single concentration of each antimicrobial was used. A growthcontrol consisting of Mueller-Hinton agar without any antimicrobial wasincluded for each run. A dark-field microscope and camera producedtime-lapse images approximately every 10 minutes of progenitor cellsgrowing into clones of daughter cells in each flowcell. The agar ensureddaughter cells were immobilized and remained localized to each growingclone. Resistant clones grew while susceptible clones arrest or lyseover time (FIG. 90).

Custom image analysis software assigned unique spatial XY coordinates toindividual progenitor cells (FIG. 91), allowing each growing cloneacross a series of time-lapse images to be identified. The intensity ofeach clone can be used as a metric of clone mass in each image. Bymeasuring over time, this metric was used to generate growth curves foreach individual clone (FIG. 92). The pattern of cell responses was usedto determine the susceptibility of the overall population. Computeralgorithms developed for each organism-antimicrobial combination convertmicrobial growth response into an MIC value using a mathematicalregression model based on the response of isolates with known MICs for asingle-concentration of a given antimicrobial. Once obtained, MIC valueswere interpreted using FDA, CLSI or EUCAST breakpoints along with expertrules to determine the categorical result (susceptible (S), intermediate(I) or resistant (R)). In addition to MICs, this technique was appliedto phenotypic resistance mechanism detection.

In certain embodiments, the determination of antimicrobialsusceptibility may further comprise subjecting identified microorganismsto antimicrobial susceptibility analysis, wherein the microorganisms aregrown in Mueller Hinton nutrient-depleted media to differentiateantimicrobial-resistant cells from filamentous,antimicrobial-susceptible cells within about 12 hours of growth. In someembodiments, fastidious microorganisms are grown in 1% phytone tryptoseMueller Hinton Agar for determination of antimicrobial susceptibilityand/or minimum inhibitory concentration of antimicrobials.

Polymicrobial Sample Analysis

FISH ID was performed in several different flowcells. Target channelsenabled the identification of multiple target species as well as thedetection of non-target species in polymicrobial samples. Antimicrobialsusceptibility testing was performed on up to two target organisms inthe same sample.

Cell morphokinetic image analysis was used for species recognition inpolymicrobial infections, and enabled the analysis software to assign anMIC to each species during AST testing. Morphokinetic features includingcell morphology (FIG. 93A), division rates, growth patterns and signalintensity were used to distinguish organism species.

Quantitative FISH assay results provided an estimation of the relativeabundance of multiple species in polymicrobial samples so both speciescan be diluted to the appropriate concentration for AST testing in thesame test run. However, if dilution of one species causes the otherspecies to be out of range, only the AST results for the in-rangespecies was reported. In addition, if morphokinetic features wereinsufficient to distinguish multiple species from one another, MICvalues were not reported, and additional susceptibility testing wasrequired.

Performance of Multiplexed Automated Digital Microscopy

The performance of multiplexed automated digital microscopy for positiveblood culture samples was evaluated. Clinical isolates were seeded intosimulated positive blood cultures, grown overnight, and run directly ona custom engineering iteration of the instrument. FISH ID performanceshowed 98% sensitivity (192/195 target organisms) and 99% specificity(211/214 non-target organisms) (Table 3). Antimicrobial susceptibilitytesting results showed 96% agreement with ‘gold standard’ brothmicrodilution MIC results for 520/542 representativeorganism-antimicrobial combinations (Table 4). Additional results showedaccurate phenotypic detection of 49/50 Staphylococcus aureus isolateswith resistance mechanisms (98% sensitivity) and 87/90 Staphylococcusaureus isolates without (97% specificity) (Table 5). A comprehensivelisting of Gram positive and Gram negative bacteria from an ID/ASTreference panel are presented in Tables 6 and 7. Fungi (Candida albicansand Candida glabrata) are identified only in the ID/AST reference panel.

TABLE 3 Performance of multiplexed automated digital microscopy for theidentification of clinical isolates seeded into simulated positive bloodcultures compared to known identification references. Target GroupSensitivity Specificity Staphylococcus aureus 16/16 (100%)  16/16 (100%)Staphylococcus lugdunensis 8/8 (100%) 12/12 (100%) coagulase-negative14/16 (88%)   16/16 (100%) staphylococci Enterococcus faecalis 8/8(100%)  8/8 (100%) Enterococcus faecium ^(a) 8/8 (100%)  8/8 (100%)Streptococcus genus 18/18 (100%)  12/12 (100%) Streptococcus pneumoniae6/6 (100%)  7/7 (100%) Streptococcus agalactiae 8/8 (100%)  5/6 (83%)Streptococcus pyogenes 8/8 (100%)  7/7 (100%) Escherichia coli 12/12(100%)  22/22 (100%) Klebsiella oxytoca + 15/16 (94%)   18/19 (95%)  K.pneumoniae Enterobacter aerogenes + 14/14 (100%)  16/16 (100%) E.cloacae Citrobacter freundii + 12/12 (100%)  20/20 (100%) C. koseriProteus mirabilis + 15/15 (100%)   4/4 (100%) P. vulgaris Serratiamarcescens 7/7 (100%) 17/18 (94%)  Acinetobacter baumannii 15/15 (100%)  8/8 (100%) Pseudomonas aeruginosa 8/8 (100%) 15/15 (100%) Total 192/195(98%)    211/214 (99%)   ^(a) Enterococcus faecium and otherEnterococcus spp., not differentiated, excluding Enterococcus faecalis

TABLE 4 Comparison of multiplexed automated digital microscopy with CLSIstandard frozen broth microdilution for antimicrobial susceptibilitytesting of clinical isolates seeded into simulated positive bloodcultures. Organism Antibiotic Essential Agreement Staphylococcus aureusDoxycycline 45/47 (96%) Staphylococcus aureus Ceftaroline 43/44 (98%)Streptococcus pneumoniae Penicillin 27/28 (96%) Streptococcus pneumoniaeCeftriaxone 18/19 (95%) Enterococcus spp. Ampicillin 43/45 (96%)Enterococcus spp. Vancomycin 44/48 (92%) Enterococcus spp. Linezolid44/47 (94%) Pseudomonas aeruginosa Ciprofloxacin  47/47 (100%)Pseudomonas aeruginosa Amikacin  48/48 (100%) Acinetobacter baumanniiCiprofloxacin 39/41 (95%) Acinetobacter baumannii Amikacin 37/40 (93%)Acinetobacter baumannii Imipenem 44/45 (98%) Acinetobacter baumanniiMinocycline 41/43 (95%) Total 520/542 (96%) 

TABLE 5 Performance of multiplexed automated digital microscopy for thedetection of phenotypic resistance mechanisms in S. aureus clinicalisolates seeded into simulated positive blood cultures. ResistanceMechanism Sensitivity Specificity MRSA 24/24 (100%)  22/22 (100%) VRSA12/12 (100%) 41/43 (95%) MLSb 13/14 (93%)  24/25 (96%) Total 49/50(98%)  87/90 (97%) MRSA = methicillin-resistant S. aureus; VRSA =vancomycin-resistant S. aureus; MLSb =macrolide-lincosamide-streptogramin b resistance.

TABLE 6 Accelerate ID/AST System test panel for Gram-positive bloodculture samples. Trimethoprim- Organism Ampicillin PenicillinCeftaroline Ceftriaxone Doxycycline Levofloxacin ErythromycinSulfamethoxazole S. aureus X X X X S. lugdunensis X X X CONS spp.^(b) XX X E. faecalis X X E. faecium ^(c) X X S. pneumoniae X X X X S.pyogenes ^(a) S. agalactiae ^(a) Streptococcus spp.^(ad) ResistancePhenotype MLSb HLAR (Erythromycin- (High-Level HLAR (High-Level OrganismDaptomycin Linezolid Vancomycin MRSA (Cefoxitin) Clindamycin)Gentamicin) Streptomycin) S. aureus X X X X X S. lugdunensis X X X X XCONS spp.^(b) X X X X X E. faecalis X X X X X E. faecium ^(c) X X X X XS. pneumoniae X S. pyogenes ^(a) S. agalactiae ^(a) Streptococcusspp.^(ad) MRSA = methicillin-resistant S. aureus; MLSb =macrolide-lincosamide-streptogramin b resistance; HLAR = high-levelaminoglycoside resistance ^(a)ID only ^(b)Coagulase-negativeStaphylococcus species (Staphylococcus epidermidis, Staphylococcushaemolyticus, Staphylococcus hominis, Staphylococcus capitis, notdifferentiated) ^(c) Enterococcus faecium and other Enterococcus spp.,not differentiated, excluding Enterococcus faecalis ^(d) Streptococcusmitis, Streptococcus pyogenes, Streptococcus gallolyticus, Streptococcusagalactiae, Streptococcus pneumoniae, not differentiated

TABLE 7 Accelerate ID/AST System test panel for Gram-negative bloodculture samples. Organism Ampicillin-Sulbactam Piperacillin-TazobactamCefazolin Cefepime Ceftazidime Ceftriaxone Ertapenem Imipenem E. coli XX X X X X X Klebsiella spp.^(a) X X X X X X X Enterobacter spp.^(b) X XX X X Proteus spp.^(c) X X X X X X Citrobacter spp.^(d) X X X X X S.marcescens X X X X X P. aeruginosa X X X X A. baumannii X X X X OrganismMeropenem Amikacin Gentamicin Tobramycin Ciprofloxacin MinocyclineAztreonam Colistin E. coli X X X X X X X Klebsiella spp.^(a) X X X X X XX Enterobacter spp.^(b) X X X X X X X Proteus spp.^(c) X X X X X XCitrobacter spp.^(d) X X X X X X X S. marcescens X X X X P. aeruginosa XX X X X X X A. baumannii X X X X X ^(a) Klebsiella oxytoca + K.pneumoniae ^(b) Enterobacter aerogenes + E. cloacae ^(c) Proteusmirabilis + P. vulgaris ^(d) Citrobacter freundii + C. koseri

FIG. 101 depicts a generalized example of a suitable computingenvironment 10100 in which the described innovations may be implemented.The computing environment 10100 is not intended to suggest anylimitation as to scope of use or functionality, as the innovations maybe implemented in diverse general-purpose or special-purpose computingsystems. For example, the computing environment 10100 can be any of avariety of computing devices (e.g., desktop computer, laptop computer,server computer, tablet computer, etc.) Alternatively, the computingenvironment 10100 can be part of the control board 321 of FIG. 3.

With reference to FIG. 101, the computing environment 10100 includes oneor more processing units 10110, 10115 and memory 10120, 10125. In FIG.101, this basic configuration 10130 is included within a dashed line.The processing units 10110, 10115 execute computer-executableinstructions. A processing unit can be a general-purpose centralprocessing unit (CPU), processor in an application-specific integratedcircuit (ASIC) or any other type of processor. In a multi-processingsystem, multiple processing units execute computer-executableinstructions to increase processing power. For example, FIG. 101 shows acentral processing unit 10110 as well as a graphics processing unit orco-processing unit 10115. The tangible memory 10120, 10125 may bevolatile memory (e.g., registers, cache, RAM), non-volatile memory(e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two,accessible by the processing unit(s). The memory 10120, 10125 storessoftware 10180 implementing one or more innovations described herein, inthe form of computer-executable instructions suitable for execution bythe processing unit(s).

In any of the examples described herein, computing environment 10100 canbe a system controller attached to an instrument and operable to controlthe instrument.

A computing system may have additional features. For example, thecomputing environment 10100 includes storage 10140, one or more inputdevices 10150, one or more output devices 10160, and one or morecommunication connections 10170. An interconnection mechanism (notshown) such as a bus, controller, or network interconnects thecomponents of the computing environment 10100. Typically, operatingsystem software (not shown) provides an operating environment for othersoftware executing in the computing environment 10100, and coordinatesactivities of the components of the computing environment 10100.

The tangible storage 10140 may be removable or non-removable, andincludes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, orany other medium which can be used to store information in anon-transitory way and which can be accessed within the computingenvironment 10100. The storage 10140 stores instructions for thesoftware 10180 implementing one or more innovations described herein.

The input device(s) 10150 may be a touch input device such as akeyboard, mouse, pen, or trackball, a voice input device, a scanningdevice, or another device that provides input to the computingenvironment 10100. The output device(s) 10160 may be a display, printer,speaker, CD-writer, or another device that provides output from thecomputing environment 10100.

The communication connection(s) 10170 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can use an electrical, optical, RF, or other carrier.

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed methods can be used in conjunction with other methods.

Any of the disclosed methods can be implemented as computer-executableinstructions stored on one or more computer-readable storage media(e.g., one or more optical media discs, volatile memory components (suchas DRAM or SRAM), or non-volatile memory components (such as flashmemory or hard drives)) and executed on a computer (e.g., anycommercially available computer, including smart phones or other mobiledevices that include computing hardware). The term computer-readablestorage media does not include communication connections, such assignals and carrier waves. Any of the computer-executable instructionsfor implementing the disclosed techniques as well as any data createdand used during implementation of the disclosed embodiments can bestored on one or more computer-readable storage media. Thecomputer-executable instructions can be part of, for example, adedicated software application or a software application that isaccessed or downloaded via a web browser or other software application(such as a remote computing application). Such software can be executed,for example, on a single local computer (e.g., any suitable commerciallyavailable computer) or in a network environment (e.g., via the Internet,a wide-area network, a local-area network, a client-server network (suchas a cloud computing network), or other such network) using one or morenetwork computers.

For clarity, only certain selected aspects of the software-basedimplementations are described. Other details that are well known in theart are omitted. For example, it should be understood that the disclosedtechnology is not limited to any specific computer language or program.For instance, the disclosed technology can be implemented by softwarewritten in C++, Java, Perl, JavaScript, assembly language, or any othersuitable programming language. Likewise, the disclosed technology is notlimited to any particular computer or type of hardware. Certain detailsof suitable computers and hardware are well known and need not be setforth in detail in this disclosure.

It should also be well understood that any functionality describedherein can be performed, at least in part, by one or more hardware logiccomponents, instead of software. For example, and without limitation,illustrative types of hardware logic components that can be used includeField-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Program-specific Standard Products (ASSPs),System-on-a-chip systems (SOCs), Complex Programmable Logic Devices(CPLDs), etc.

Furthermore, any of the software-based embodiments (comprising, forexample, computer-executable instructions for causing a computer toperform any of the disclosed methods) can be uploaded, downloaded, orremotely accessed through a suitable communication means. Such suitablecommunication means include, for example, the Internet, the World WideWeb, an intranet, software applications, cable (including fiber opticcable), magnetic communications, electromagnetic communications(including RF, microwave, and infrared communications), electroniccommunications, or other such communication means.

The disclosed methods, apparatus, and systems should not be construed aslimiting in any way. Instead, the present disclosure is directed towardall novel and nonobvious features and aspects of the various disclosedembodiments, alone and in various combinations and subcombinations withone another. The disclosed methods, apparatus, and systems are notlimited to any specific aspect or feature or combination thereof, nor dothe disclosed embodiments require that any one or more specificadvantages be present or problems be solved.

In view of the many possible embodiments to which the principles of thedisclosed invention may be applied, it should be recognized that theillustrated embodiments are only examples of the disclosure and shouldnot be taken as limiting the scope of the invention. Rather, the scopeof the invention is defined by the following claims. We therefore claimas our invention all that comes within the scope of these claims.

The disclosure presented herein is believed to encompass at least onedistinct invention with independent utility. While the at least oneinvention has been disclosed in exemplary forms, the specificembodiments thereof as described and illustrated herein are not to beconsidered in a limiting sense, as numerous variation are possible.Equivalent changes, modifications and variations of the variety ofembodiments, materials, compositions, and methods may be made within thescope of the present disclosure, achieving substantially similarresults. The subject matter of the at least one invention includes allnovel and non-obvious combinations and sub-combinations of the variouselements, features, functions and/or properties disclosed herein andtheir equivalents.

The methods described herein may be implemented to facilitate rapidculturing and detection of microbial cells from samples. Benefits, otheradvantages, and solutions to problems have been described herein withregard to specific embodiments. However, the benefits, advantages,solutions to problems, and any element or combination of elements thatmay cause any benefits, advantage, or solution to occur or becomes morepronounced are not to be considered as critical, required, or essentialfeatures or elements of any or all the claims of the at least oneinvention. Many changes and modifications within the scope of theinstant disclosure may be made without departing from the spiritthereof, and the one or more inventions described herein include allsuch modifications. Corresponding structures, materials, acts, andequivalents of all elements in the claims are intended to include anystructure, material, or acts for performing the functions in combinationwith other claim elements as specifically recited. The scope of the oneor more inventions should be determined by the appended claims and theirlegal equivalents, rather than by the examples set forth herein.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent exemplary functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in a practical system. However, the benefits,advantages, solutions to problems, and any elements that may cause anybenefit, advantage, or solution to occur or become more pronounced arenot to be construed as critical, required, or essential features orelements of the inventions. The scope of the inventions is accordinglyto be limited by nothing other than the appended claims, in whichreference to an element in the singular is not intended to mean “one andonly one” unless explicitly so stated, but rather “one or more.”Moreover, where a phrase similar to “at least one of A, B, or C” is usedin the claims, it is intended that the phrase be interpreted to meanthat A alone may be present in an embodiment, B alone may be present inan embodiment, C alone may be present in an embodiment, or that anycombination of the elements A, B and C may be present in a singleembodiment; for example, A and B, A and C, B and C, or A and B and C.Different cross-hatching is used throughout the figures to denotedifferent parts but not necessarily to denote the same or differentmaterials.

Systems, methods and apparatus are provided herein. In the detaileddescription herein, references to “one embodiment,” “an embodiment,” “anexample embodiment,” etc., indicate that the embodiment described mayinclude a particular feature, structure, or characteristic, but everyembodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed. After reading the description, it will be apparent to oneskilled in the relevant art(s) how to implement the disclosure inalternative embodiments.

Furthermore, no element, component, or method step in the presentdisclosure is intended to be dedicated to the public regardless ofwhether the element, component, or method step is explicitly recited inthe claims. No claim element herein is to be construed under theprovisions of 35 U.S.C. 112(f), unless the element is expressly recitedusing the phrase “means for.” As used herein, the terms “comprises,”“comprising,” or any other variation thereof, are intended to cover anon-exclusive inclusion, such that a process, method, article, orapparatus that comprises a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus.

We claim:
 1. A system for autofocusing on a microorganism to be imaged,comprising: a first light source for projecting a beam of light andgenerating a reflective spot on an image plane; a second light sourcefor projecting light to capture an image of the microorganism on theimage plane; a movable objective positioned between the first and secondlight sources and the image plane for allowing focusing on the imageplane, the movable objective being movable to a first out-of-focusposition having a first focal point a first predetermined distance infront of the image plane and to a second out-of-focus position having asecond focal point a second predetermined distance behind the imageplane; an image sensor for capturing the reflective spot and forcapturing the image of the microorganism using the objective to focus;and a controller for receiving two or more images of the reflective spotfrom the image sensor, wherein the two or more images are captured withthe objective in at least the first and second out-of-focus positions,and for modifying a position of the objective based on intensityweightings of the reflective spots to an in-focus position.
 2. Thesystem of claim 1, wherein the intensity weightings relate to agreyscale value associated with each pixel that is weighted according toeach pixel's position relative to a center.
 3. The system of claim 1,wherein the controller is configured to perform the following: perform aprescan wherein intensity weightings over a plurality of focus positionsof the objective are acquired.
 4. The system of claim 1, wherein thecontroller is configured to perform the following: perform a prescanwherein offset versus focus position is obtained for a plurality offocus positions of the objective.
 5. The system of claim 1, wherein thecontroller is configured to perform the following: calculate a focusposition using the intensity weightings of the reflective spots togetherwith the prescan of the intensity weightings to determine the positionof the objective.